eBay:iPads:: sold classification:: color
bdanalytics
Date: (Wed) Jul 29, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "color_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold"
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
# return(log(raw))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA))## [1] Y Y N N <NA>
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
# return(exp(var))
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, NA)))## [1] 1 1 0 0 NAif ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
glb_id_var <- c("UniqueID")
glb_category_var <- c("prdline.my")
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
glb_derive_lst[["idseq.my"]] <- list(
mapfn=function(UniqueID) { return(UniqueID - 10000) }
, args=c("UniqueID"))
glb_derive_lst[["prdline.my"]] <- list(
mapfn=function(productline) { return(productline) }
, args=c("productline"))
glb_derive_lst[["startprice.log"]] <- list(
mapfn=function(startprice) { return(log(startprice)) }
, args=c("startprice"))
# glb_derive_lst[["startprice.log.zval"]] <- list(
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
mod_raw <- gsub("(\\w)(\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 128 gb ", " 128gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" brokenCharger ", " broken Charger ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" conditon|condtion|conditions", " condition", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" drop ", " dropped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCLOUD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bspace (grey|gray)", "spacegray", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPACOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" UnlockedCracked ", " Unlocked Cracked ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glb_txt_vars <- c("descr.my")
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
glb_append_stop_words[["descr.my"]] <- c(NULL
# freq = 1
,"511","825","975"
,"2nd"
,"a1314","a1430","a1432"
,"abused","across","adaptor","add","advised","antenna","anti","anyone","anything"
,"applied","area","arizona","att"
,"backlight","beetle","beginning","besides","bidder","bonus","boot","bound","bruises"
,"capacity","changed","changing","chrome","closely"
,"confidence","considerable","consumer","contents","control","cream","cuts"
,"daily","date","daughter","decent","defender","defense","degree","depicted"
,"disclaimer","distressed","divider"
,"dlxnqat9g5wt","done","dont","durable","dust","duty"
,"either","emblem","erased","ereader","essentially","every","exact","exhibition"
,"facing","faint","february","film","final","flickers","folding","forgot","forwarders"
,"games","generic","genuine","glitter","goes","grey"
,"half","handstand","hdmi","high","higher","hole","hospital"
,"immaculate","impact","instead","intended","interior","intro"
,"jack","july"
,"keeps","kids","kind","known"
,"largest","last","late","let","letters","level"
,"lifting","limited","line","lining","liquidation"
,"local","long","longer","looping","loose","loss"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll","mgye2ll"
,"middle", "mind","mixed","mostly"
,"neither","none","november"
,"occasional","oem","online","outside"
,"paperwork","past","period","pet","photograph","piece","played","plug"
,"poor","portfolio","portion","pouch","preinstalled","price","proof","provided"
,"ranging","rather"
,"real","realized","reassemble","receipt","recently","red"
,"reflected","refunds","remote","repeat"
,"required","reserve","residue","restarts","result","reviewed"
,"ringer","roughly","running"
,"said","school"
,"seamlessly","seconds","seem","semi","send","september","serious","setup"
,"shell","short","site","size","sleeve","slice","smoke","smooth","smudge"
,"softer","software","somewhat","soon"
,"space","sparingly","sparkiling","special","speed"
,"stains","standup","status","stopped","strictly"
,"subtle","sustained","swappacom","swivel"
,"take","technical","tempered","texture","thank","therefore","think","though"
,"toddler","totally","touchy","toys","tried","typical"
,"university","unknown","untouched","upgrade"
,"valid","vary","version"
,"want","website","whole","winning","wrapped"
,"zaag","zero", "zombie","zoogue"
)
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_filter_txt_terms <- "top" # or "sparse"
glb_top_n <- c(10)
names(glb_top_n) <- glb_txt_vars
glb_sprs_thresholds <- c(0.950) # Generates 10 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
# User-specified exclusions
glb_exclude_vars_as_features <- c("productline", "description", "startprice"
#, "startprice.log", "sold"
)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE
glb_cluster.seed <- 189 # or any integer
glb_interaction_only_features <- NULL # or ???
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "bayesglm", "glmnet", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "glmnet", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
#glb_model_evl_criteria <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL #"Low.cor.X.glm"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage")
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 14.411 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here")
glb_drop_obs <- c(
11234, #sold=0; 2 other dups(10306, 11503) are sold=1
11844, #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
NULL)
glb_allobs_df <- glb_allobs_df[!glb_allobs_df[, glb_id_var] %in% glb_drop_obs, ]
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
# !_sp
spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
stop("mismatches between spd_allobs_df & glb_allobs_df")
mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
stop("mismatches between mrg_allobs_df & glb_allobs_df")
mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
mrg_allobs_df$startprice.predict.
print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
colorcol_name = "biddable"))## Warning in myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff", :
## converting biddable to class:factor
print(myplot_histogram(mrg_allobs_df, "startprice.diff",
fill_col_name = "biddable"))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
glb_allobs_df <- mrg_allobs_df
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
"startprice.log", "startprice.predict.")
###
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Only for _sp
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
# glb_allobs_df$.src <- "Test"
# glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 14.411 26.493 12.082
## 2 inspect.data 2 0 26.493 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 999 860 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## sold sold.fctr .n
## 1 0 N 999
## 2 1 Y 860
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 999 860 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5373857 0.4626143 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, "<descriptor>")
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: startprice.diff"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 26.493 39.269 12.776
## 3 scrub.data 2 1 39.269 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1444 999
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1593 0 0 0 0 0
## 1 288 0 4 36 28 172 196
## Unknown 4 4 2 0 0 330 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1597 0 0 0 0 0
## 1 292 0 6 36 28 172 196
## Unknown 0 0 0 0 0 330 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 scrub.data 2 1 39.269 40.04 0.771
## 4 transform.data 2 2 40.041 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: idseq.my..."
## [1] "Creating new feature: prdline.my..."
## [1] "Creating new feature: startprice.log..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10178, 10948, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
glb_allobs_df[row_pos, "descr.my"] <-
gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 4 transform.data 2 2 40.041 40.684 0.643
## 5 extract.features 3 0 40.685 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 40.691 NA NA# Options:
# Select Tf, log(1 + Tf), Tf-IDF or BM25Tf-IDf
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 40.691 40.706
## 2 extract.features_factorize.str.vars 2 0 40.706 NA
## elapsed
## 1 0.015
## 2 NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid prdline.my
## "storage" "productline" ".src" ".grpid" "prdline.my"
## descr.my
## "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5## Warning: Creating factors of string variable: prdline.my: # of unique
## values: 12if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE,
preserve_intra_word_dashes=TRUE, lazy=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_DTM_terms <- function(DTM) {
TfIdf_mtrx <- as.matrix(DTM)
rownames(TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
TfIdf_vctr <- colSums(TfIdf_mtrx)
names(TfIdf_vctr) <- dimnames(DTM)[[2]]
TfIdf_df <- as.data.frame(TfIdf_vctr)
names(TfIdf_df) <- "TfIdf"
TfIdf_df$term <- rownames(TfIdf_df)
TfIdf_df$freq <- colSums(TfIdf_mtrx != 0)
TfIdf_df$pos <- 1:nrow(TfIdf_df)
return(TfIdf_df <- orderBy(~ -TfIdf, TfIdf_df))
}
get_corpus_terms <- function(txt_corpus) {
TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
return(TfIdf_df <- get_DTM_terms(TfIdf_DTM))
}
#stop(here")
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
#tolower Not needed as of version 0.6.2 ?
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english")), lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
#stop(here")
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_TfIdf_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_TfIdf_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop TfIDf terms for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.n.post.stop <- rowSums(TfIdf_stop_mtrx > 0)
tmp_allobs_df$terms.n.post.stop.log <- log(1 + tmp_allobs_df$terms.n.post.stop)
tmp_allobs_df$TfIdf.sum.post.stop <- rowSums(TfIdf_stop_mtrx)
print(sprintf(" Top_n stem TfIDf terms for %s:", txt_var))
glb_corpus_lst[[txt_var]] <- tm_map(glb_corpus_lst[[txt_var]], stemDocument,
"english", lazy=TRUE) #Features ???
print(myprint_df(full_TfIdf_df <- get_corpus_terms(glb_corpus_lst[[txt_var]]),
glb_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_TfIdf_df
TfIdf_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_corpus_lst[[txt_var]],
control=list(weighting=weightTfIdf)))
rownames(TfIdf_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]] <- TfIdf_stem_mtrx
tmp_allobs_df$terms.n.post.stem <- rowSums(TfIdf_stem_mtrx > 0)
tmp_allobs_df$terms.n.post.stem.log <- log(1 + tmp_allobs_df$terms.n.post.stem)
tmp_allobs_df$TfIdf.sum.post.stem <- rowSums(TfIdf_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.n.post.stem / tmp_allobs_df$terms.n.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio),
"terms.n.stem.stop.Ratio"] <- 1.0
tmp_allobs_df$TfIdf.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$TfIdf.sum.post.stem / tmp_allobs_df$TfIdf.sum.post.stop
tmp_allobs_df[is.nan(tmp_allobs_df$TfIdf.sum.stem.stop.Ratio),
"TfIdf.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(txt_var_pfx, c("terms.n.post.stop", "terms.n.post.stem")))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_df <- get_DTM_terms(full_TfIdf_DTM)
full_TfIdf_df <- full_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(full_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".full", sep="")
names(full_TfIdf_df) <- col_names
# full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
# rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
# full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
# names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
# full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
# names(full_TfIdf_df) <- "TfIdf.full"
# full_TfIdf_df$term <- rownames(full_TfIdf_df)
# full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
# full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_df <- get_DTM_terms(sprs_TfIdf_DTM)
sprs_TfIdf_df <- sprs_TfIdf_df[, c(2, 1, 3, 4)]
col_names <- names(sprs_TfIdf_df)
col_names[2:length(col_names)] <-
paste(col_names[2:length(col_names)], ".sprs", sep="")
names(sprs_TfIdf_df) <- col_names
# sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
# names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
# sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
# names(sprs_TfIdf_df) <- "TfIdf.sprs"
# sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
# sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
# sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_DTM_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
if (glb_filter_txt_terms == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_filter_txt_terms == "top") {
txt_X_df <- txt_full_X_df[, terms_full_df$pos[1:glb_top_n[[txt_var]]], FALSE]
} else stop("glb_filter_txt_terms should be one of c('sparse', 'top') vs. '",
glb_filter_txt_terms, "'")
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")]),
paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <- 0
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 40.706 41.029
## 3 extract.features_process.text 3 0 41.030 NA
## elapsed
## 2 0.324
## 3 NA
## [1] "Building glb_txt_lst..."
## [1] "running gsub for 10 (of 178): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 178): #\\bAlejandro G\\. Iñárritu#..."
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## [1] "running gsub for 90 (of 178): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 178): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 178): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 178): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 178): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 178): #\\bWall Street#..."
## [1] "running gsub for 150 (of 178): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 178): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 178): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 6
## [[1]]
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## [1] "ROKEN SCREEN"
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## 1 CONDITION. 8
## 2 ONLY. 6
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
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## 8 ICLOUD. 2
## 9 IPADS. 2
## 10 LOCKED. 2
## 11 LOCKS. 2
## 12 ONLY. 2
## 13 SCRATCHES. 2
## 14 TEARS. 2
## 15 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Open 3
## 4 New Condition 2
## 7 New Digitizer 1
## 8 New Opened 1
## 9 New Scratch 1
## 10 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
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## pattern .n
## 1 C Stock 3
## 2 W blue 1
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## label step_major
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## 3 0 41.030 42.748 1.718
## 4 1 42.748 NA NA
## [1] "Remaining compound terms in descr.my: "
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## 5 extract.features_build.corpus 4
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## 4 1 42.748 42.753 0.005
## 5 0 42.753 NA NA
## [1] "Building glb_corpus_lst..."
## [1] " Top_n stop TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 668; Cols: 4"
## TfIdf term freq pos
## condition 209.2728 condition 498 137
## new 126.1758 new 156 402
## used 124.5458 used 240 636
## good 121.4973 good 197 268
## scratches 114.5796 scratches 254 521
## screen 107.2911 screen 210 523
## TfIdf term freq pos
## scuffs 31.166256 scuffs 39 528
## scratching 7.623387 scratching 9 522
## including 6.588369 including 8 302
## sure 6.363052 sure 6 587
## protected 3.943444 protected 3 478
## ebay 2.305685 ebay 2 206
## TfIdf term freq pos
## blemish 1.137558 blemish 1 74
## cables 1.137558 cables 1 96
## engravement 1.137558 engravement 1 211
## handling 1.137558 handling 1 281
## mic 1.137558 mic 1 379
## 79in 1.034144 79in 1 14
## TfIdf term freq pos
## blemish 1.137558 blemish 1 74
## cables 1.137558 cables 1 96
## engravement 1.137558 engravement 1 211
## handling 1.137558 handling 1 281
## mic 1.137558 mic 1 379
## 79in 1.034144 79in 1 14## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem TfIDf terms for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] "Rows: 528; Cols: 4"
## TfIdf term freq pos
## condit 209.2617 condit 496 111
## use 147.7914 use 291 501
## scratch 129.1467 scratch 286 409
## new 126.1758 new 156 316
## good 121.5866 good 197 213
## ipad 108.6364 ipad 232 249
## TfIdf term freq pos
## near 21.703837 near 34 311
## name 3.920486 name 3 310
## <db><cf> 3.602633 <db><cf> 2 17
## pin 3.227959 pin 2 358
## happen 2.593896 happen 2 224
## appli 1.625083 appli 1 43
## TfIdf term freq pos
## marksabsolut 1.421948 marksabsolut 1 292
## often 1.421948 often 1 326
## 360 1.263954 360 1 9
## 975 1.137558 975 1 15
## mic 1.137558 mic 1 298
## 79in 1.034144 79in 1 14
## TfIdf term freq pos
## marksabsolut 1.421948 marksabsolut 1 292
## often 1.421948 often 1 326
## 360 1.263954 360 1 9
## 975 1.137558 975 1 15
## mic 1.137558 mic 1 298
## 79in 1.034144 79in 1 14## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) charact## [,1]
## terms.n.post.stop -0.086024184
## terms.n.post.stop.log -0.066489274
## TfIdf.sum.post.stop -0.034561568
## terms.n.post.stem -0.085753719
## terms.n.post.stem.log -0.066421421
## TfIdf.sum.post.stem -0.037435134
## terms.n.stem.stop.Ratio 0.020583201
## TfIdf.sum.stem.stop.Ratio -0.009352534
## label step_major step_minor bgn end
## 5 extract.features_build.corpus 4 0 42.753 53.408
## 6 extract.features_extract.DTM 5 0 53.408 NA
## elapsed
## 5 10.655
## 6 NA
## [1] "Extracting TfIDf terms for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor bgn end elapsed
## 6 extract.features_extract.DTM 5 0 53.408 55.493 2.086
## 7 extract.features_report.DTM 6 0 55.494 NA NA
## [1] "Reporting TfIDf terms for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 528)>>
## Non-/sparse entries: 8212/1394684
## Sparsity : 99%
## Maximal term length: 16
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2657, terms: 8)>>
## Non-/sparse entries: 2069/19187
## Sparsity : 90%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
## colorcol_name = "in.sprs"): converting in.sprs to class:factor
## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df,
## terms_TfIdf_df): object 'full_TfIdf_mtrx' not found## label step_major step_minor bgn end elapsed
## 7 extract.features_report.DTM 6 0 55.494 57.239 1.745
## 8 extract.features_bind.DTM 7 0 57.239 NA NA
## [1] "Binding DTM for descr.my..."
## label step_major step_minor bgn end elapsed
## 8 extract.features_bind.DTM 7 0 57.239 57.645 0.407
## 9 extract.features_bind.DXM 8 0 57.646 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# Use model info provided in description
mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)## UniqueID sold.fctr prdline.my sold .grpid color condition cellular
## 618 10618 Y iPad mini 1 <NA> Black Used 0
## 940 10940 N iPad 3 0 <NA> Black Used 1
## 2472 12474 <NA> Unknown NA <NA> Unknown Used Unknown
## carrier storage
## 618 None 16
## 940 Verizon 16
## 2472 Unknown Unknown
## descr.my
## 618 Nice Apple iPad Mini 16GB Wi- Fi 7.9" spacegray MF432LL/ A A1432 Locked It does work just cannot
## 940 LIKE NEW (MODEL A1430) + BLUETOOTH KEYBOARD (LATEST MODEL A1314), LEATHER CREAM SMART COVER, BLACK
## 2472 here we have spacegray apple ipad mini a1432 no charger works great has small nicks nothing majorglb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)## UniqueID sold.fctr prdline.my sold .grpid color
## 617 10617 Y iPad 2 1 <NA> White
## 618 10618 Y iPad mini 1 <NA> Black
## 992 10992 N iPad 2 0 <NA> White
## 1105 11105 N iPad mini Retina 0 <NA> Gold
## 1359 11360 N iPad 3 0 <NA> Unknown
## 1360 11361 Y Unknown 1 <NA> Unknown
## 1365 11366 Y iPad 1 1 <NA> Unknown
## 2637 12639 <NA> iPad 2 NA <NA> Black
## condition cellular carrier storage
## 617 Used 0 None 64
## 618 Used 0 None 16
## 992 Used 0 None 16
## 1105 Used 0 None 16
## 1359 Used Unknown Unknown Unknown
## 1360 Used Unknown Unknown Unknown
## 1365 Used Unknown Unknown Unknown
## 2637 For parts or not working 0 None 64
## descr.my
## 617 This a used Apple iPad 2 64GB, Wi- Fi, 9.7in - White (MC991LL/ A) shows signs of wear, has been
## 618 Nice Apple iPad Mini 16GB Wi- Fi 7.9" spacegray MF432LL/ A A1432 Locked It does work just cannot
## 992 Up for auction is this APPLE iPad 1st Gen Model MB292LL 16 GB of Memory Storage 9.7" touch screen
## 1105 Like New Condition Apple iPad Mini 3 MGYE2LL/ A 16GB Wi- Fi Gold Version Tablet/ eReader. Includes USB
## 1359 iPad 3 Black 64Gb storage Model Mc707ll/ a iPad is in very nice shape, glass and case
## 1360 APPLE iPAD AIR 32GB WHITE MD789LL/ B WHITE. This item is Previously Lightly Used, in Good Condition.
## 1365 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 2637 IPAD 2 64GB BLACK MODEL MC916LL/ A WIFI ONLY MODEL. PICTURE OF IPAD IS ACTUAL UNIT YOU WILL RECEIVE.glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
glb_rsp_var)))## prdline.my productline D.P.mini sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad 2 iPad 2 0 N 139
## 4 iPad mini iPad mini 0 N 138
## 5 iPad mini iPad mini 0 Y 126
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 121
## 8 iPad mini iPad mini 0 <NA> 108
## 9 iPad Air iPad Air 0 N 102
## 10 iPad 1 iPad 1 0 N 100
## 11 iPad Air 2 iPad Air 2 0 N 100
## 12 iPad 4 iPad 4 0 N 93
## 13 Unknown Unknown 0 <NA> 89
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 80
## 17 iPad Air iPad Air 0 Y 78
## 18 iPad Air iPad Air 0 <NA> 74
## 19 iPad 3 iPad 3 0 N 73
## 20 iPad Air 2 iPad Air 2 0 Y 71
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad Air 2 iPad Air 2 0 <NA> 62
## 24 iPad mini 3 iPad mini 3 0 N 61
## 25 iPad mini 2 iPad mini 2 0 N 56
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 52
## 28 iPad mini 2 iPad mini 2 0 Y 48
## 29 iPad mini 3 iPad mini 3 0 <NA> 35
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad mini iPad mini 1 N 7
## 32 iPad mini iPad mini 1 Y 5
## 33 iPad mini 2 iPad mini 2 1 <NA> 4
## 34 iPad mini Retina iPad mini Retina 0 Y 4
## 35 iPad mini iPad mini 1 <NA> 3
## 36 iPad mini 3 iPad mini 3 1 <NA> 3
## 37 iPad mini Retina iPad mini Retina 0 N 3
## 38 Unknown Unknown 1 <NA> 2
## 39 iPad mini 2 iPad mini 2 1 N 2
## 40 iPad mini 3 iPad mini 3 1 N 2
## 41 Unknown Unknown 1 N 1
## 42 Unknown Unknown 1 Y 1
## 43 iPad 5 iPad 5 0 Y 1
## 44 iPad Air Unknown 0 Y 1
## 45 iPad mini Unknown 1 <NA> 1
## 46 iPad mini iPad mini 2 Y 1
## 47 iPad mini 2 iPad mini 2 1 Y 1
## 48 iPad mini Retina iPad mini Retina 1 N 1print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
(glb_allobs_df$D.P.mini > 0),
c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])## UniqueID prdline.my sold .grpid color condition
## 1172 11172 Unknown 0 8 Unknown Used
## 1803 11804 Unknown 1 <NA> White Seller refurbished
## 2223 12225 Unknown NA 8 Unknown Used
## 2472 12474 iPad mini NA <NA> Space Gray Used
## 2623 12625 Unknown NA <NA> White For parts or not working
## cellular carrier storage
## 1172 Unknown Unknown 16
## 1803 1 AT&T Unknown
## 2223 Unknown Unknown 16
## 2472 0 None Unknown
## 2623 Unknown Unknown Unknown
## descr.my
## 1172 IPAD mini . not sure of what generation it can be. selling as is or best offer. had a crack but
## 1803 30 Day Warranty. Refurbished iPad Mini with signs of normal wear including possible scratching on
## 2223 IPAD mini . not sure of what generation it can be. selling as is or best offer. had a crack but
## 2472 here we have spacegray apple ipad mini a1432 no charger works great has small nicks nothing major
## 2623 Lot of 10 mixed iPad minis. Colors, models & storage capacity vary between each lot. There may beglb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
"prdline.my"] <- "iPad mini"
print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
glb_rsp_var)))## prdline.my productline D.P.air sold.fctr .n
## 1 iPad 2 iPad 2 0 <NA> 154
## 2 iPad 2 iPad 2 0 Y 147
## 3 iPad mini iPad mini 0 N 145
## 4 iPad 2 iPad 2 0 N 139
## 5 iPad mini iPad mini 0 Y 132
## 6 iPad 1 iPad 1 0 Y 125
## 7 Unknown Unknown 0 N 120
## 8 iPad mini iPad mini 0 <NA> 111
## 9 iPad 1 iPad 1 0 N 100
## 10 iPad Air iPad Air 0 N 98
## 11 iPad Air 2 iPad Air 2 0 N 97
## 12 iPad 4 iPad 4 0 N 92
## 13 Unknown Unknown 0 <NA> 88
## 14 iPad 1 iPad 1 0 <NA> 88
## 15 Unknown Unknown 0 Y 80
## 16 iPad 3 iPad 3 0 Y 79
## 17 iPad Air iPad Air 0 Y 75
## 18 iPad 3 iPad 3 0 N 73
## 19 iPad Air iPad Air 0 <NA> 73
## 20 iPad Air 2 iPad Air 2 0 Y 69
## 21 iPad 4 iPad 4 0 <NA> 68
## 22 iPad 4 iPad 4 0 Y 64
## 23 iPad mini 3 iPad mini 3 0 N 63
## 24 iPad Air 2 iPad Air 2 0 <NA> 60
## 25 iPad mini 2 iPad mini 2 0 N 58
## 26 iPad 3 iPad 3 0 <NA> 55
## 27 iPad mini 2 iPad mini 2 0 <NA> 55
## 28 iPad mini 2 iPad mini 2 0 Y 49
## 29 iPad mini 3 iPad mini 3 0 <NA> 38
## 30 iPad mini 3 iPad mini 3 0 Y 27
## 31 iPad Air iPad Air 1 N 4
## 32 iPad mini Retina iPad mini Retina 0 N 4
## 33 iPad mini Retina iPad mini Retina 0 Y 4
## 34 iPad Air iPad Air 1 Y 3
## 35 iPad mini Unknown 0 <NA> 3
## 36 iPad Air 2 iPad Air 2 1 <NA> 2
## 37 iPad Air 2 iPad Air 2 1 N 2
## 38 iPad Air 2 iPad Air 2 1 Y 2
## 39 Unknown Unknown 1 <NA> 1
## 40 Unknown Unknown 1 N 1
## 41 iPad 3 iPad 3 1 Y 1
## 42 iPad 4 iPad 4 1 N 1
## 43 iPad 5 iPad 5 0 Y 1
## 44 iPad Air Unknown 1 Y 1
## 45 iPad Air iPad Air 1 <NA> 1
## 46 iPad Air 2 iPad Air 2 2 N 1
## 47 iPad mini Unknown 0 N 1
## 48 iPad mini Unknown 0 Y 1
## 49 iPad mini 2 iPad mini 2 1 <NA> 1print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
(glb_allobs_df$D.P.air > 0),
c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])## UniqueID prdline.my sold .grpid color condition cellular carrier
## 946 10946 Unknown 0 <NA> Unknown Used Unknown Unknown
## 1360 11361 iPad Air 1 <NA> White Used 0 None
## 2433 12435 Unknown NA <NA> Space Gray Used Unknown Unknown
## storage
## 946 Unknown
## 1360 32
## 2433 128
## descr.my
## 946 Gently used apple iPad Air, no scratches on screen and almost no visible wear on back of item. No
## 1360 APPLE iPAD AIR 32GB WHITE MD789LL/ B WHITE. This item is Previously Lightly Used, in Good Condition.
## 2433 ***128gb*** black/ spacegray iPad Air excellent used condition(no scratches, dents, or blemishes)#glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
"prdline.my"] <- "iPad Air"
print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
c(glb_id_var, "sold",
"prdline.my", "color", "condition", "cellular", "carrier", "storage", "descr.my")])## UniqueID sold prdline.my color condition cellular
## 1766 11767 0 Unknown Unknown For parts or not working Unknown
## 1810 11811 0 Unknown Black Seller refurbished 0
## 2154 12156 NA Unknown Black Used 0
## carrier storage
## 1766 Unknown Unknown
## 1810 None Unknown
## 2154 None 32
## descr.my
## 1766 Ipad 2 32gb Housing. Some scratches and small dents, but overall good condition.
## 1810 30 Day Warranty. Refurbished iPad 2 with scratching on screen and wear on back plate. Comes with
## 2154 Original IPAD 1st generation - used one owner (myself)Good shape as pictured. Fully functional asglb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
names(tmp_allobs_df) <- "old.prdline.my"
glb_allobs_df$prdline.my <-
plyr::revalue(glb_allobs_df$prdline.my, c(
# "iPad 1" = "iPad",
# "iPad 2" = "iPad2+",
"iPad 3" = "iPad 3+",
"iPad 4" = "iPad 3+",
"iPad 5" = "iPad 3+",
"iPad Air" = "iPadAir",
"iPad Air 2" = "iPadAir",
"iPad mini" = "iPadmini",
"iPad mini 2" = "iPadmini 2+",
"iPad mini 3" = "iPadmini 2+",
"iPad mini Retina" = "iPadmini 2+"
))
tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))## prdline.my old.prdline.my .n
## 1 iPad 2 iPad 2 442
## 2 iPadmini iPad mini 393
## 3 iPad 1 iPad 1 314
## 4 Unknown Unknown 285
## 5 iPadAir iPad Air 257
## 6 iPadAir iPad Air 2 233
## 7 iPad 3+ iPad 4 225
## 8 iPad 3+ iPad 3 208
## 9 iPadmini 2+ iPad mini 2 163
## 10 iPadmini 2+ iPad mini 3 128
## 11 iPadmini 2+ iPad mini Retina 8
## 12 iPad 3+ iPad 5 1print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))## prdline.my .n
## 1 iPadAir 490
## 2 iPad 2 442
## 3 iPad 3+ 434
## 4 iPadmini 393
## 5 iPad 1 314
## 6 iPadmini 2+ 299
## 7 Unknown 285#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))## color D.P.black D.P.gold D.P.spacegray D.P.white .n
## 1 Unknown 0 0 0 0 1017
## 2 Unknown 0 0 0 1 4
## 3 Unknown 1 0 0 0 4
## 4 Unknown 0 0 1 0 1
## 5 Unknown 1 0 0 1 1print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.black sold prdline.my condition cellular carrier
## 631 10631 Unknown 1 1 iPad 2 Used 1 AT&T
## 683 10683 Unknown 1 0 iPad 2 Used 0 None
## 858 10858 Unknown 1 1 iPad 3+ Used 0 None
## 1243 11244 Unknown 1 0 Unknown Used Unknown Unknown
## 2135 12137 Unknown 1 NA iPad 1 Used 1 AT&T
## storage
## 631 16
## 683 32
## 858 16
## 1243 Unknown
## 2135 16
## descr.my
## 631 Very good condition. Minor bumps and bruises. Only scratches on screen are in non- viewing black
## 683 Comes with folding black case and is engraved in small letters on the back. Still works perfectly
## 858 screen cracked. name engraving in the back (blacked out)
## 1243 Ipad is in fair condition. Minor scratches on back. Edge around screen is black instead of white.
## 2135 Device is in AVERAGE used cosmetic condition with heavy scratches and wear. Color is black . Device isglb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.spacegray prdline.my condition cellular carrier
## 2104 12106 Unknown 1 iPadAir Used 0 None
## storage
## 2104 16
## descr.my
## 2104 This is an iPad Air first generation (spacegray color). It's a used iPad (just like new) as shown in theglb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
"cellular", "carrier", "storage", "descr.my")])## UniqueID color D.P.white prdline.my condition
## 573 10573 Unknown 1 iPadmini 2+ Used
## 809 10809 Unknown 1 iPad 3+ Used
## 925 10925 Unknown 1 iPadmini 2+ Used
## 1243 11244 Unknown 1 Unknown Used
## 1734 11735 Unknown 1 iPad 3+ For parts or not working
## cellular carrier storage
## 573 0 None 16
## 809 0 None 64
## 925 0 None 64
## 1243 Unknown Unknown Unknown
## 1734 1 Verizon 16
## descr.my
## 573 Like new white iPad mini no scratches always kept in case, sold with keyboard, box and cords
## 809 iPad 3 gen. 64GB, white, wifi- only. Condition = good as new, very minor sign of use. No charger.
## 925 iPad mini 2/ Retina Display/ Latest Model/ 64GB/ Wi- Fi/ Silver&White . Near Mint Condition excellent
## 1243 Ipad is in fair condition. Minor scratches on back. Edge around screen is black instead of white.
## 1734 Device is in POOR used cosmetic condition with cracked outer glass. Color is White. Device isglb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
"White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 extract.features_bind.DXM 8 0 57.646 123.755 66.109
## 10 extract.features_end 9 0 123.756 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 6 extract.features_extract.DTM 5
## 7 extract.features_report.DTM 6
## 3 extract.features_process.text 3
## 8 extract.features_bind.DTM 7
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor bgn end elapsed duration
## 9 0 57.646 123.755 66.109 66.109
## 5 0 42.753 53.408 10.655 10.655
## 6 0 53.408 55.493 2.086 2.085
## 7 0 55.494 57.239 1.745 1.745
## 3 0 41.030 42.748 1.718 1.718
## 8 0 57.239 57.645 0.407 0.406
## 2 0 40.706 41.029 0.324 0.323
## 1 0 40.691 40.706 0.015 0.015
## 4 1 42.748 42.753 0.005 0.005
## [1] "Total Elapsed Time: 123.755 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 extract.features 3 0 40.685 125.107 84.422
## 6 cluster.data 4 0 125.107 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 6 cluster.data 4 0 125.107 126.073 0.966
## 7 manage.missing.data 4 1 126.074 NA NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1600 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2427
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air D.P.black D.P.white
## 2636 2640 2647
## D.P.gold D.P.spacegray
## 2655 2650
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1600 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2427
## D.npnct01.log D.npnct02.log D.npnct03.log
## 2579 2657 2614
## D.npnct04.log D.npnct05.log D.npnct06.log
## 2657 2592 2554
## D.npnct07.log D.npnct08.log D.npnct09.log
## 2656 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct17.log D.npnct18.log
## 2546 2657 2656
## D.npnct19.log D.npnct20.log D.npnct21.log
## 2657 2657 2657
## D.npnct22.log D.npnct23.log D.npnct24.log
## 2657 2657 1520
## D.npnct25.log D.npnct26.log D.npnct27.log
## 2657 2657 2657
## D.npnct28.log D.npnct29.log D.npnct30.log
## 2649 2657 2657
## D.nstopwrds.log D.P.http D.P.mini
## 1663 2657 2623
## D.P.air D.P.black D.P.white
## 2636 2640 2647
## D.P.gold D.P.spacegray
## 2655 2650
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520Step 4.1: manage missing data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print("Clustering features: ")
print(cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_rsp_var]), method="ML")))
category_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, glb_rsp_var]))
names(category_df)[c(1, 2)] <- c(glb_category_var, glb_rsp_var)
category_df <- do.call(tidyr::spread, list(category_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(category_df),
function(row) entropy(as.numeric(category_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(category_df),
function(row) sum(as.numeric(category_df[row, -1])))
category_df$.entropy <- tmp.entropy; category_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)", glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
print(category_df)
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (grp in sort(unique(glb_allobs_df[, glb_category_var]))) {
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_rsp_var, glb_category_var, glb_txt_vars, cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df[, glb_category_var].fctr) / minClusterSize=20)
# which(levels(glb_allobs_df[, glb_category_var].fctr) == grp)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
cluster_df <- as.data.frame(table(glb_allobs_df[, glb_category_var],
glb_allobs_df[, ".clusterid"],
glb_allobs_df[, glb_rsp_var]))
cluster_df <- subset(cluster_df, Freq > 0)
names(cluster_df)[c(1, 2, 3)] <- c(glb_category_var, ".clusterid", glb_rsp_var)
# spread(unite(cluster_df, prdline.my.clusterid, prdline.my, .clusterid),
# sold.fctr, Freq)
cluster_df <- do.call(tidyr::unite,
list(cluster_df, paste0(glb_category_var, ".clusterid"),
grep(glb_category_var, names(cluster_df)),
grep(".clusterid", names(cluster_df))))
cluster_df <- do.call(tidyr::spread,
list(cluster_df, glb_rsp_var, "Freq"))
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
print(cluster_df)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features[paste0(glb_category_var, ".fctr")] <-
c(".clusterid.fctr")
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr## [1] "Clustering features: "
## [1] "D.T.condit" "D.T.use" "D.T.scratch" "D.T.new"
## [5] "D.T.good" "D.T.ipad" "D.T.screen" "D.T.great"
## [9] "D.T.work" "D.T.excel" "D.P.http" "D.P.mini"
## [13] "D.P.air" "D.P.black" "D.P.white" "D.P.gold"
## [17] "D.P.spacegray"
## [1] "glb_allobs_df Entropy: 0.6903"
## [1] "glb_allobs_df$prdline.my Entropy: 0.6850 (99.2280 pct)"
## prdline.my N Y .entropy .knt
## 1 Unknown 118 80 0.6746159 198
## 2 iPad 1 100 125 0.6869616 225
## 3 iPad 2 141 147 0.6929302 288
## 4 iPad 3+ 166 145 0.6908657 311
## 5 iPadAir 203 150 0.6818332 353
## 6 iPadmini 146 133 0.6920612 279
## 7 iPadmini 2+ 125 80 0.6688571 205
## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 5 10005 N Unknown
## 130 10130 Y Unknown
## descr.my
## 5 Please feel free to buy. All product have been thoroughly inspected, cleaned and tested to be 100%
## 130 New - Open Box. Charger included.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 5 0 0 0 0.0000000 0 0 0
## 130 0 0 0 0.8180361 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 5 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 5 0 0 0
## 130 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 1029 11029 N Unknown
## 1077 11077 N Unknown
## descr.my
## 1029 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## 1077 A device listed in near mint used cosmetic condition with light blemishes from use. Housing &
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1029 0.220126 0.5801286 0 0 0 0 0
## 1077 0.220126 0.5801286 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 1029 0 0 0 0 0 0 0
## 1077 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 1029 0 0 0
## 1077 0 0 0
## [1] "Category: iPad 1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 9 10009 Y iPad 1
## 13 10013 Y iPad 1
## descr.my
## 9
## 13 GOOD CONDITION. CLEAN ICLOUD. NO LOCKS. CLEAN IMEI. This tablet has been fully tested and works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 9 0.000000 0 0 0 0.0000000 0 0
## 13 0.220126 0 0 0 0.3412301 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 9 0 0.000000 0 0 0 0 0
## 13 0 0.340566 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 9 0 0 0
## 13 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 32 10032 Y iPad 1
## 1163 11163 N iPad 1
## descr.my
## 32 In very good condition, does show sign of use but mostly had a case on at all times. Still has a
## 1163 Device is in GOOD used cosmetic condition with normal wear, engraving on back. Device is in 100%
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad
## 32 0.3026733 0.3988384 0 0 0.4691913 0
## 1163 0.2201260 0.2900643 0 0 0.3412301 0
## D.T.screen D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 32 0 0 0 0 0 0 0
## 1163 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 32 0 0 0 0
## 1163 0 0 0 0
## [1] "Category: iPad 2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 1 10001 N iPad 2
## 2 10002 Y iPad 2
## descr.my
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 1 0.8071287 0.0000000 0.0000000 0 0 1.172534 0.0000000
## 2 0.0000000 0.5801286 0.2923374 0 0 0.000000 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 1 0 0 0
## 2 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 132 10132 N iPad 2
## 2382 12384 <NA> iPad 2
## descr.my
## 132 Overall good condition. Some wear from use. Scratches/ scuffs/ nicks/ scrapes on unit housing back,
## 2382 Device is in GOOD used cosmetic condition with normal scratches & wear, engravement on the housing.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad
## 132 0.2017822 0.2658923 0.2679759 0 0.3127942 0
## 2382 0.2421386 0.3190707 0.3215711 0 0.3753531 0
## D.T.screen D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air
## 132 0 0 0 0 0 0 0
## 2382 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 132 0 0 0 0
## 2382 0 0 0 0
## [1] "Category: iPad 3+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 3 10003 Y iPad 3+
## 11 10011 Y iPad 3+
## descr.my
## 3
## 11 good condition, minor wear and tear on body some light scratches on screen. functions great.
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 3 0.000000 0 0.0000000 0 0.0000000 0 0.0000000
## 11 0.220126 0 0.2923374 0 0.3412301 0 0.3309884
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 3 0.0000000 0 0 0 0 0 0
## 11 0.4008907 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 3 0 0 0
## 11 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 40 10040 N iPad 3+
## 1602 11603 Y iPad 3+
## descr.my
## 40 Item has been professionally tested and inspected. Tests show that all features work correctly. This
## 1602 Work fine iCloud lock
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 40 0 0 0 0 0 0 0
## 1602 0 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 40 0 0.4162473 0 0 0 0 0
## 1602 0 0.9365565 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 40 0 0 0
## 1602 0 0 0
## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 16 10016 N iPadAir
## 33 10033 N iPadAir
## descr.my
## 16
## 33 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 16 0 0 0 0 0.000000 0.0000000 0
## 33 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 16 0 0 0 0 0 0 0
## 33 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 16 0 0 0
## 33 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 44 10044 N iPadAir
## 1166 11166 N iPadAir
## descr.my
## 44 Open Box Units Grade A Condition. Units may contain minor cosmetic imperfections.
## 1166 Immaculate Condition. . In a Box
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 44 0.220126 0 0 0 0 0 0
## 1166 1.210693 0 0 0 0 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 44 0 0 0 0 0 0 0
## 1166 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 44 0 0 0
## 1166 0 0 0
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 7 10007 Y iPadmini
## 76 10076 Y iPadmini
## descr.my
## 7
## 76 Works perfectly, NOT iCloud locked, 1 owner. It is in not in very good condition, but works
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 7 0.0000000 0 0 0 0.0000000 0 0
## 76 0.3026733 0 0 0 0.4691913 0 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 7 0 0.0000000 0 0 0 0 0
## 76 0 0.9365565 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 7 0 0 0
## 76 0 0 0
## [1] "min distance(-0.0000) pair:"
## UniqueID sold.fctr prdline.my
## 491 10491 N iPadmini
## 2564 12566 <NA> iPadmini
## descr.my
## 491 Cracked screen, flaw is shown in picture, everything is fully functional and
## 2564 daughter dropped cracked screen. got some water in the cracks. screen lights but you can't see
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 491 0 0 0 0 0 0 0.4551091
## 2564 0 0 0 0 0 0 0.8090829
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 491 0 0 0 0 0 0 0
## 2564 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 491 0 0 0
## 2564 0 0 0
## [1] "Category: iPadmini 2+"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdline.my
## 4 10004 N iPadmini 2+
## 18 10018 N iPadmini 2+
## descr.my
## 4
## 18 We are selling good quality iPads that have been fully tested by an Apple Certified Technician. The
## D.T.condit D.T.use D.T.scratch D.T.new D.T.good D.T.ipad D.T.screen
## 4 0 0 0 0 0.000000 0.0000000 0
## 18 0 0 0 0 0.417059 0.3908446 0
## D.T.great D.T.work D.T.excel D.P.http D.P.mini D.P.air D.P.black
## 4 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 4 0 0 0
## 18 0 0 0
## [1] "min distance(0.0000) pair:"
## UniqueID sold.fctr prdline.my descr.my D.T.condit D.T.use D.T.scratch
## 4 10004 N iPadmini 2+ 0 0 0
## 6 10006 Y iPadmini 2+ 0 0 0
## D.T.new D.T.good D.T.ipad D.T.screen D.T.great D.T.work D.T.excel
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 4 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## [1] "glb_allobs_df$prdline.my$.clusterid Entropy: 0.6732 (98.2710 pct)"
## prdline.my.clusterid N Y .entropy .knt
## 1 Unknown_1 77 53 0.6760076 130
## 2 Unknown_2 27 23 0.6899438 50
## 3 Unknown_3 14 4 0.5297062 18
## 4 iPad 1_1 62 88 0.6780488 150
## 5 iPad 1_2 13 16 0.6877868 29
## 6 iPad 1_3 13 13 0.6931472 26
## 7 iPad 1_4 12 8 0.6730117 20
## 8 iPad 2_1 69 88 0.6858064 157
## 9 iPad 2_2 46 40 0.6907115 86
## 10 iPad 2_3 21 11 0.6434916 32
## 11 iPad 2_4 5 8 0.6662784 13
## 12 iPad 3+_1 77 97 0.6865267 174
## 13 iPad 3+_2 28 17 0.6629658 45
## 14 iPad 3+_3 21 9 0.6108643 30
## 15 iPad 3+_4 11 12 0.6922017 23
## 16 iPad 3+_5 16 8 0.6365142 24
## 17 iPad 3+_6 13 2 0.3926745 15
## 18 iPadAir_1 142 106 0.6825740 248
## 19 iPadAir_2 38 37 0.6930583 75
## 20 iPadAir_3 23 7 0.5432728 30
## 21 iPadmini 2+_1 100 61 0.6635142 161
## 22 iPadmini 2+_2 16 9 0.6534182 25
## 23 iPadmini 2+_3 9 10 0.6917615 19
## 24 iPadmini_1 99 87 0.6910646 186
## 25 iPadmini_2 17 14 0.6884572 31
## 26 iPadmini_3 11 18 0.6637255 29
## 27 iPadmini_4 12 10 0.6890092 22
## 28 iPadmini_5 7 4 0.6554818 11# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 manage.missing.data 4 1 126.074 129.394 3.32
## 8 select.features 5 0 129.395 NA NAStep 5.0: select features
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.000000e+00
## biddable biddable 5.481788e-01
## startprice.log startprice.log -4.674275e-01
## startprice startprice -4.569767e-01
## startprice.predict. startprice.predict. -3.861675e-01
## startprice.diff startprice.diff -3.078989e-01
## UniqueID UniqueID -1.895466e-01
## idseq.my idseq.my -1.895466e-01
## condition.fctr condition.fctr -1.535490e-01
## D.npnct05.log D.npnct05.log -1.180559e-01
## D.terms.n.post.stop D.terms.n.post.stop -8.602418e-02
## D.terms.n.post.stem D.terms.n.post.stem -8.575372e-02
## D.npnct14.log D.npnct14.log -7.862038e-02
## cellular.fctr cellular.fctr -7.432974e-02
## D.terms.n.post.stop.log D.terms.n.post.stop.log -6.648927e-02
## D.terms.n.post.stem.log D.terms.n.post.stem.log -6.642142e-02
## D.nwrds.unq.log D.nwrds.unq.log -6.642142e-02
## .clusterid .clusterid -6.638659e-02
## .clusterid.fctr .clusterid.fctr -6.638659e-02
## D.ndgts.log D.ndgts.log -6.286847e-02
## D.npnct09.log D.npnct09.log -6.182533e-02
## carrier.fctr carrier.fctr -5.990892e-02
## D.npnct12.log D.npnct12.log -5.932565e-02
## D.nwrds.log D.nwrds.log -5.903215e-02
## D.ratio.nstopwrds.nwrds D.ratio.nstopwrds.nwrds 5.811054e-02
## D.nchrs.log D.nchrs.log -5.653921e-02
## D.nuppr.log D.nuppr.log -5.539161e-02
## D.npnct28.log D.npnct28.log -5.245832e-02
## D.npnct06.log D.npnct06.log -4.997620e-02
## D.npnct15.log D.npnct15.log 4.840228e-02
## D.npnct24.log D.npnct24.log -4.584500e-02
## D.npnct16.log D.npnct16.log -4.494040e-02
## D.nstopwrds.log D.nstopwrds.log -4.468510e-02
## prdline.my.fctr prdline.my.fctr -4.158143e-02
## D.npnct08.log D.npnct08.log -3.965131e-02
## color.fctr color.fctr -3.913729e-02
## D.T.new D.T.new -3.806364e-02
## D.TfIdf.sum.post.stem D.TfIdf.sum.post.stem -3.743513e-02
## D.sum.TfIdf D.sum.TfIdf -3.743513e-02
## D.npnct13.log D.npnct13.log -3.734631e-02
## D.T.condit D.T.condit -3.689700e-02
## D.TfIdf.sum.post.stop D.TfIdf.sum.post.stop -3.456157e-02
## D.P.gold D.P.gold -3.044917e-02
## D.T.excel D.T.excel 2.672297e-02
## D.npnct03.log D.npnct03.log 2.576379e-02
## D.T.screen D.T.screen 2.523744e-02
## D.npnct07.log D.npnct07.log 2.500407e-02
## D.npnct10.log D.npnct10.log -2.410150e-02
## D.npnct18.log D.npnct18.log -2.152502e-02
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 2.058320e-02
## D.npnct11.log D.npnct11.log -1.920355e-02
## D.P.white D.P.white 1.848988e-02
## D.T.use D.T.use 1.492486e-02
## D.T.work D.T.work -1.263445e-02
## storage.fctr storage.fctr -1.167550e-02
## D.T.ipad D.T.ipad -1.165928e-02
## D.P.mini D.P.mini -1.124183e-02
## D.TfIdf.sum.stem.stop.Ratio D.TfIdf.sum.stem.stop.Ratio -9.352534e-03
## D.P.air D.P.air -9.262995e-03
## D.T.great D.T.great 8.157329e-03
## D.ratio.sum.TfIdf.nwrds D.ratio.sum.TfIdf.nwrds 7.670839e-03
## D.T.scratch D.T.scratch -7.005676e-03
## .rnorm .rnorm 6.756274e-03
## D.npnct01.log D.npnct01.log 4.125530e-03
## D.P.spacegray D.P.spacegray 3.481857e-03
## D.P.black D.P.black -1.248546e-03
## D.T.good D.T.good -9.510709e-05
## D.npnct02.log D.npnct02.log NA
## D.npnct04.log D.npnct04.log NA
## D.npnct17.log D.npnct17.log NA
## D.npnct19.log D.npnct19.log NA
## D.npnct20.log D.npnct20.log NA
## D.npnct21.log D.npnct21.log NA
## D.npnct22.log D.npnct22.log NA
## D.npnct23.log D.npnct23.log NA
## D.npnct25.log D.npnct25.log NA
## D.npnct26.log D.npnct26.log NA
## D.npnct27.log D.npnct27.log NA
## D.npnct29.log D.npnct29.log NA
## D.npnct30.log D.npnct30.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.000000e+00
## biddable 0 5.481788e-01
## startprice.log 1 4.674275e-01
## startprice 1 4.569767e-01
## startprice.predict. 1 3.861675e-01
## startprice.diff 0 3.078989e-01
## UniqueID 1 1.895466e-01
## idseq.my 0 1.895466e-01
## condition.fctr 0 1.535490e-01
## D.npnct05.log 0 1.180559e-01
## D.terms.n.post.stop 0 8.602418e-02
## D.terms.n.post.stem 0 8.575372e-02
## D.npnct14.log 0 7.862038e-02
## cellular.fctr 0 7.432974e-02
## D.terms.n.post.stop.log 0 6.648927e-02
## D.terms.n.post.stem.log 0 6.642142e-02
## D.nwrds.unq.log 0 6.642142e-02
## .clusterid 1 6.638659e-02
## .clusterid.fctr 0 6.638659e-02
## D.ndgts.log 0 6.286847e-02
## D.npnct09.log 0 6.182533e-02
## carrier.fctr 0 5.990892e-02
## D.npnct12.log 0 5.932565e-02
## D.nwrds.log 0 5.903215e-02
## D.ratio.nstopwrds.nwrds 0 5.811054e-02
## D.nchrs.log 0 5.653921e-02
## D.nuppr.log 0 5.539161e-02
## D.npnct28.log 0 5.245832e-02
## D.npnct06.log 0 4.997620e-02
## D.npnct15.log 0 4.840228e-02
## D.npnct24.log 0 4.584500e-02
## D.npnct16.log 0 4.494040e-02
## D.nstopwrds.log 0 4.468510e-02
## prdline.my.fctr 0 4.158143e-02
## D.npnct08.log 0 3.965131e-02
## color.fctr 0 3.913729e-02
## D.T.new 1 3.806364e-02
## D.TfIdf.sum.post.stem 0 3.743513e-02
## D.sum.TfIdf 0 3.743513e-02
## D.npnct13.log 0 3.734631e-02
## D.T.condit 1 3.689700e-02
## D.TfIdf.sum.post.stop 0 3.456157e-02
## D.P.gold 1 3.044917e-02
## D.T.excel 1 2.672297e-02
## D.npnct03.log 0 2.576379e-02
## D.T.screen 1 2.523744e-02
## D.npnct07.log 0 2.500407e-02
## D.npnct10.log 0 2.410150e-02
## D.npnct18.log 0 2.152502e-02
## D.terms.n.stem.stop.Ratio 0 2.058320e-02
## D.npnct11.log 0 1.920355e-02
## D.P.white 1 1.848988e-02
## D.T.use 1 1.492486e-02
## D.T.work 1 1.263445e-02
## storage.fctr 0 1.167550e-02
## D.T.ipad 1 1.165928e-02
## D.P.mini 1 1.124183e-02
## D.TfIdf.sum.stem.stop.Ratio 0 9.352534e-03
## D.P.air 1 9.262995e-03
## D.T.great 1 8.157329e-03
## D.ratio.sum.TfIdf.nwrds 0 7.670839e-03
## D.T.scratch 1 7.005676e-03
## .rnorm 0 6.756274e-03
## D.npnct01.log 0 4.125530e-03
## D.P.spacegray 1 3.481857e-03
## D.P.black 1 1.248546e-03
## D.T.good 1 9.510709e-05
## D.npnct02.log 0 NA
## D.npnct04.log 0 NA
## D.npnct17.log 0 NA
## D.npnct19.log 0 NA
## D.npnct20.log 0 NA
## D.npnct21.log 0 NA
## D.npnct22.log 0 NA
## D.npnct23.log 0 NA
## D.npnct25.log 0 NA
## D.npnct26.log 0 NA
## D.npnct27.log 0 NA
## D.npnct29.log 0 NA
## D.npnct30.log 0 NA
## D.P.http 1 NA# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## [1] "cor(D.TfIdf.sum.post.stem, D.sum.TfIdf)=1.0000"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0374"
## [1] "cor(sold.fctr, D.sum.TfIdf)=-0.0374"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.sum.TfIdf as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stem.log)=1.0000"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0664"
## [1] "cor(sold.fctr, D.terms.n.post.stem.log)=-0.0664"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem.log as highly correlated
## with D.nwrds.unq.log## [1] "cor(D.nwrds.unq.log, D.terms.n.post.stop.log)=0.9999"
## [1] "cor(sold.fctr, D.nwrds.unq.log)=-0.0664"
## [1] "cor(sold.fctr, D.terms.n.post.stop.log)=-0.0665"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.unq.log as highly correlated with
## D.terms.n.post.stop.log## [1] "cor(D.nchrs.log, D.nuppr.log)=0.9995"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nuppr.log)=-0.0554"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nuppr.log as highly correlated with
## D.nchrs.log## [1] "cor(D.terms.n.post.stem, D.terms.n.post.stop)=0.9991"
## [1] "cor(sold.fctr, D.terms.n.post.stem)=-0.0858"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0860"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stem as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.TfIdf.sum.post.stop)=0.9976"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0374"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stop)=-0.0346"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stop as highly correlated with
## D.TfIdf.sum.post.stem## [1] "cor(D.nchrs.log, D.nwrds.log)=0.9929"
## [1] "cor(sold.fctr, D.nchrs.log)=-0.0565"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0590"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nchrs.log as highly correlated with
## D.nwrds.log## [1] "cor(D.nwrds.log, D.terms.n.post.stop.log)=0.9921"
## [1] "cor(sold.fctr, D.nwrds.log)=-0.0590"
## [1] "cor(sold.fctr, D.terms.n.post.stop.log)=-0.0665"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nwrds.log as highly correlated with
## D.terms.n.post.stop.log## [1] "cor(D.terms.n.post.stop, D.terms.n.post.stop.log)=0.9755"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0860"
## [1] "cor(sold.fctr, D.terms.n.post.stop.log)=-0.0665"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.n.post.stop.log as highly correlated
## with D.terms.n.post.stop## [1] "cor(D.TfIdf.sum.post.stem, D.npnct24.log)=0.9648"
## [1] "cor(sold.fctr, D.TfIdf.sum.post.stem)=-0.0374"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0458"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.TfIdf.sum.post.stem as highly correlated with
## D.npnct24.log## [1] "cor(D.npnct24.log, D.ratio.nstopwrds.nwrds)=-0.9620"
## [1] "cor(sold.fctr, D.npnct24.log)=-0.0458"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0581"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct24.log as highly correlated with
## D.ratio.nstopwrds.nwrds## [1] "cor(D.npnct06.log, D.npnct16.log)=0.9556"
## [1] "cor(sold.fctr, D.npnct06.log)=-0.0500"
## [1] "cor(sold.fctr, D.npnct16.log)=-0.0449"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct16.log as highly correlated with
## D.npnct06.log## [1] "cor(D.nstopwrds.log, D.terms.n.post.stop)=0.8885"
## [1] "cor(sold.fctr, D.nstopwrds.log)=-0.0447"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0860"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.nstopwrds.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(D.ratio.nstopwrds.nwrds, D.terms.n.post.stop)=-0.8670"
## [1] "cor(sold.fctr, D.ratio.nstopwrds.nwrds)=0.0581"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0860"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.nstopwrds.nwrds as highly correlated
## with D.terms.n.post.stop## [1] "cor(D.npnct13.log, D.terms.n.post.stop)=0.7357"
## [1] "cor(sold.fctr, D.npnct13.log)=-0.0373"
## [1] "cor(sold.fctr, D.terms.n.post.stop)=-0.0860"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.npnct13.log as highly correlated with
## D.terms.n.post.stop## [1] "cor(carrier.fctr, cellular.fctr)=0.7131"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0599"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0743"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## id cor.y exclude.as.feat cor.y.abs
## 76 sold 1.000000e+00 1 1.000000e+00
## 69 biddable 5.481788e-01 0 5.481788e-01
## 60 D.ratio.nstopwrds.nwrds 5.811054e-02 0 5.811054e-02
## 40 D.npnct15.log 4.840228e-02 0 4.840228e-02
## 12 D.T.excel 2.672297e-02 1 2.672297e-02
## 28 D.npnct03.log 2.576379e-02 0 2.576379e-02
## 18 D.T.screen 2.523744e-02 1 2.523744e-02
## 32 D.npnct07.log 2.500407e-02 0 2.500407e-02
## 67 D.terms.n.stem.stop.Ratio 2.058320e-02 0 2.058320e-02
## 10 D.P.white 1.848988e-02 1 1.848988e-02
## 19 D.T.use 1.492486e-02 1 1.492486e-02
## 14 D.T.great 8.157329e-03 1 8.157329e-03
## 61 D.ratio.sum.TfIdf.nwrds 7.670839e-03 0 7.670839e-03
## 3 .rnorm 6.756274e-03 0 6.756274e-03
## 26 D.npnct01.log 4.125530e-03 0 4.125530e-03
## 9 D.P.spacegray 3.481857e-03 1 3.481857e-03
## 13 D.T.good -9.510709e-05 1 9.510709e-05
## 5 D.P.black -1.248546e-03 1 1.248546e-03
## 17 D.T.scratch -7.005676e-03 1 7.005676e-03
## 4 D.P.air -9.262995e-03 1 9.262995e-03
## 23 D.TfIdf.sum.stem.stop.Ratio -9.352534e-03 0 9.352534e-03
## 8 D.P.mini -1.124183e-02 1 1.124183e-02
## 15 D.T.ipad -1.165928e-02 1 1.165928e-02
## 81 storage.fctr -1.167550e-02 0 1.167550e-02
## 20 D.T.work -1.263445e-02 1 1.263445e-02
## 36 D.npnct11.log -1.920355e-02 0 1.920355e-02
## 43 D.npnct18.log -2.152502e-02 0 2.152502e-02
## 35 D.npnct10.log -2.410150e-02 0 2.410150e-02
## 6 D.P.gold -3.044917e-02 1 3.044917e-02
## 22 D.TfIdf.sum.post.stop -3.456157e-02 0 3.456157e-02
## 11 D.T.condit -3.689700e-02 1 3.689700e-02
## 38 D.npnct13.log -3.734631e-02 0 3.734631e-02
## 21 D.TfIdf.sum.post.stem -3.743513e-02 0 3.743513e-02
## 62 D.sum.TfIdf -3.743513e-02 0 3.743513e-02
## 16 D.T.new -3.806364e-02 1 3.806364e-02
## 72 color.fctr -3.913729e-02 0 3.913729e-02
## 33 D.npnct08.log -3.965131e-02 0 3.965131e-02
## 75 prdline.my.fctr -4.158143e-02 0 4.158143e-02
## 56 D.nstopwrds.log -4.468510e-02 0 4.468510e-02
## 41 D.npnct16.log -4.494040e-02 0 4.494040e-02
## 49 D.npnct24.log -4.584500e-02 0 4.584500e-02
## 31 D.npnct06.log -4.997620e-02 0 4.997620e-02
## 53 D.npnct28.log -5.245832e-02 0 5.245832e-02
## 57 D.nuppr.log -5.539161e-02 0 5.539161e-02
## 24 D.nchrs.log -5.653921e-02 0 5.653921e-02
## 58 D.nwrds.log -5.903215e-02 0 5.903215e-02
## 37 D.npnct12.log -5.932565e-02 0 5.932565e-02
## 70 carrier.fctr -5.990892e-02 0 5.990892e-02
## 34 D.npnct09.log -6.182533e-02 0 6.182533e-02
## 25 D.ndgts.log -6.286847e-02 0 6.286847e-02
## 1 .clusterid -6.638659e-02 1 6.638659e-02
## 2 .clusterid.fctr -6.638659e-02 0 6.638659e-02
## 59 D.nwrds.unq.log -6.642142e-02 0 6.642142e-02
## 64 D.terms.n.post.stem.log -6.642142e-02 0 6.642142e-02
## 66 D.terms.n.post.stop.log -6.648927e-02 0 6.648927e-02
## 71 cellular.fctr -7.432974e-02 0 7.432974e-02
## 39 D.npnct14.log -7.862038e-02 0 7.862038e-02
## 63 D.terms.n.post.stem -8.575372e-02 0 8.575372e-02
## 65 D.terms.n.post.stop -8.602418e-02 0 8.602418e-02
## 30 D.npnct05.log -1.180559e-01 0 1.180559e-01
## 73 condition.fctr -1.535490e-01 0 1.535490e-01
## 68 UniqueID -1.895466e-01 1 1.895466e-01
## 74 idseq.my -1.895466e-01 0 1.895466e-01
## 78 startprice.diff -3.078989e-01 0 3.078989e-01
## 80 startprice.predict. -3.861675e-01 1 3.861675e-01
## 77 startprice -4.569767e-01 1 4.569767e-01
## 79 startprice.log -4.674275e-01 1 4.674275e-01
## 7 D.P.http NA 1 NA
## 27 D.npnct02.log NA 0 NA
## 29 D.npnct04.log NA 0 NA
## 42 D.npnct17.log NA 0 NA
## 44 D.npnct19.log NA 0 NA
## 45 D.npnct20.log NA 0 NA
## 46 D.npnct21.log NA 0 NA
## 47 D.npnct22.log NA 0 NA
## 48 D.npnct23.log NA 0 NA
## 50 D.npnct25.log NA 0 NA
## 51 D.npnct26.log NA 0 NA
## 52 D.npnct27.log NA 0 NA
## 54 D.npnct29.log NA 0 NA
## 55 D.npnct30.log NA 0 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## 76 <NA> 1.161628 0.10758472 FALSE FALSE
## 69 <NA> 1.221027 0.10758472 FALSE FALSE
## 60 D.terms.n.post.stop 14.078947 4.41097364 FALSE FALSE
## 40 <NA> 153.416667 0.16137708 FALSE TRUE
## 12 <NA> 149.666667 0.75309306 FALSE TRUE
## 28 <NA> 83.227273 0.16137708 FALSE TRUE
## 18 <NA> 51.727273 0.80688542 FALSE TRUE
## 32 <NA> 1858.000000 0.10758472 FALSE TRUE
## 67 <NA> 77.869565 0.48413125 FALSE TRUE
## 10 <NA> 231.250000 0.16137708 FALSE TRUE
## 19 <NA> 48.617647 0.96826251 FALSE TRUE
## 14 <NA> 118.400000 0.80688542 FALSE TRUE
## 61 <NA> 63.000000 34.85745024 FALSE FALSE
## 3 <NA> 1.000000 100.00000000 FALSE FALSE
## 26 <NA> 52.970588 0.32275417 FALSE TRUE
## 9 <NA> 463.750000 0.10758472 FALSE TRUE
## 13 <NA> 46.540541 0.86067778 FALSE TRUE
## 5 <NA> 168.000000 0.10758472 FALSE TRUE
## 17 <NA> 47.314286 0.86067778 FALSE TRUE
## 4 <NA> 122.866667 0.16137708 FALSE TRUE
## 23 <NA> 65.176471 32.92092523 FALSE FALSE
## 8 <NA> 91.900000 0.16137708 FALSE TRUE
## 15 <NA> 56.466667 0.80688542 FALSE TRUE
## 81 <NA> 2.725146 0.26896181 FALSE FALSE
## 20 <NA> 68.720000 0.69930070 FALSE TRUE
## 36 <NA> 9.374269 0.37654653 FALSE FALSE
## 43 <NA> 1858.000000 0.10758472 FALSE TRUE
## 35 <NA> 308.666667 0.16137708 FALSE TRUE
## 6 <NA> 928.500000 0.10758472 FALSE TRUE
## 22 D.TfIdf.sum.post.stem 63.000000 34.37331899 FALSE FALSE
## 11 <NA> 24.868852 0.91447015 FALSE TRUE
## 38 D.terms.n.post.stop 5.203065 0.32275417 FALSE FALSE
## 21 D.npnct24.log 63.000000 34.31952663 FALSE FALSE
## 62 D.TfIdf.sum.post.stem 63.000000 34.31952663 FALSE FALSE
## 16 <NA> 125.071429 0.80688542 FALSE TRUE
## 72 <NA> 1.544053 0.26896181 FALSE FALSE
## 33 <NA> 69.576923 0.21516945 FALSE TRUE
## 75 <NA> 1.135048 0.37654653 FALSE FALSE
## 56 D.terms.n.post.stop 13.916667 0.80688542 FALSE FALSE
## 41 D.npnct06.log 31.245614 0.16137708 FALSE TRUE
## 49 D.ratio.nstopwrds.nwrds 1.356147 0.10758472 FALSE FALSE
## 31 <NA> 33.735849 0.16137708 FALSE TRUE
## 53 <NA> 463.250000 0.16137708 FALSE TRUE
## 57 D.nchrs.log 18.807018 4.41097364 FALSE FALSE
## 24 D.nwrds.log 15.970149 5.70199032 FALSE FALSE
## 58 D.terms.n.post.stop.log 12.738095 1.29101668 FALSE FALSE
## 37 <NA> 27.246154 0.21516945 FALSE TRUE
## 70 cellular.fctr 3.220290 0.37654653 FALSE FALSE
## 34 <NA> 308.333333 0.21516945 FALSE TRUE
## 25 <NA> 27.047619 0.69930070 FALSE TRUE
## 1 <NA> 3.536657 0.32275417 FALSE FALSE
## 2 <NA> 3.536657 0.32275417 FALSE FALSE
## 59 D.terms.n.post.stop.log 8.568000 0.80688542 FALSE FALSE
## 64 D.nwrds.unq.log 8.568000 0.80688542 FALSE FALSE
## 66 D.terms.n.post.stop 8.637097 0.80688542 FALSE FALSE
## 71 <NA> 2.116190 0.16137708 FALSE FALSE
## 39 <NA> 35.333333 0.26896181 FALSE TRUE
## 63 D.terms.n.post.stop 8.568000 0.80688542 FALSE FALSE
## 65 <NA> 8.637097 0.80688542 FALSE FALSE
## 30 <NA> 40.311111 0.10758472 FALSE TRUE
## 73 <NA> 4.003460 0.32275417 FALSE FALSE
## 68 <NA> 1.000000 100.00000000 FALSE FALSE
## 74 <NA> 1.000000 100.00000000 FALSE FALSE
## 78 <NA> 1.000000 100.00000000 FALSE FALSE
## 80 <NA> 1.000000 100.00000000 FALSE FALSE
## 77 <NA> 2.807692 30.17751479 FALSE FALSE
## 79 <NA> 2.807692 30.17751479 FALSE FALSE
## 7 <NA> 0.000000 0.05379236 TRUE TRUE
## 27 <NA> 0.000000 0.05379236 TRUE TRUE
## 29 <NA> 0.000000 0.05379236 TRUE TRUE
## 42 <NA> 0.000000 0.05379236 TRUE TRUE
## 44 <NA> 0.000000 0.05379236 TRUE TRUE
## 45 <NA> 0.000000 0.05379236 TRUE TRUE
## 46 <NA> 0.000000 0.05379236 TRUE TRUE
## 47 <NA> 0.000000 0.05379236 TRUE TRUE
## 48 <NA> 0.000000 0.05379236 TRUE TRUE
## 50 <NA> 0.000000 0.05379236 TRUE TRUE
## 51 <NA> 0.000000 0.05379236 TRUE TRUE
## 52 <NA> 0.000000 0.05379236 TRUE TRUE
## 54 <NA> 0.000000 0.05379236 TRUE TRUE
## 55 <NA> 0.000000 0.05379236 TRUE TRUE
## myNearZV is.cor.y.abs.low
## 76 FALSE FALSE
## 69 FALSE FALSE
## 60 FALSE FALSE
## 40 FALSE FALSE
## 12 FALSE FALSE
## 28 FALSE FALSE
## 18 FALSE FALSE
## 32 TRUE FALSE
## 67 FALSE FALSE
## 10 FALSE FALSE
## 19 FALSE FALSE
## 14 FALSE FALSE
## 61 FALSE FALSE
## 3 FALSE FALSE
## 26 FALSE TRUE
## 9 FALSE TRUE
## 13 FALSE TRUE
## 5 FALSE TRUE
## 17 FALSE FALSE
## 4 FALSE FALSE
## 23 FALSE FALSE
## 8 FALSE FALSE
## 15 FALSE FALSE
## 81 FALSE FALSE
## 20 FALSE FALSE
## 36 FALSE FALSE
## 43 TRUE FALSE
## 35 FALSE FALSE
## 6 TRUE FALSE
## 22 FALSE FALSE
## 11 FALSE FALSE
## 38 FALSE FALSE
## 21 FALSE FALSE
## 62 FALSE FALSE
## 16 FALSE FALSE
## 72 FALSE FALSE
## 33 FALSE FALSE
## 75 FALSE FALSE
## 56 FALSE FALSE
## 41 FALSE FALSE
## 49 FALSE FALSE
## 31 FALSE FALSE
## 53 FALSE FALSE
## 57 FALSE FALSE
## 24 FALSE FALSE
## 58 FALSE FALSE
## 37 FALSE FALSE
## 70 FALSE FALSE
## 34 FALSE FALSE
## 25 FALSE FALSE
## 1 FALSE FALSE
## 2 FALSE FALSE
## 59 FALSE FALSE
## 64 FALSE FALSE
## 66 FALSE FALSE
## 71 FALSE FALSE
## 39 FALSE FALSE
## 63 FALSE FALSE
## 65 FALSE FALSE
## 30 FALSE FALSE
## 73 FALSE FALSE
## 68 FALSE FALSE
## 74 FALSE FALSE
## 78 FALSE FALSE
## 80 FALSE FALSE
## 77 FALSE FALSE
## 79 FALSE FALSE
## 7 TRUE NA
## 27 TRUE NA
## 29 TRUE NA
## 42 TRUE NA
## 44 TRUE NA
## 45 TRUE NA
## 46 TRUE NA
## 47 TRUE NA
## 48 TRUE NA
## 50 TRUE NA
## 51 TRUE NA
## 52 TRUE NA
## 54 TRUE NA
## 55 TRUE NA#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing missing values (geom_point).
print(subset(glb_feats_df, myNearZV))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 32 D.npnct07.log 0.02500407 0 0.02500407 <NA>
## 43 D.npnct18.log -0.02152502 0 0.02152502 <NA>
## 6 D.P.gold -0.03044917 1 0.03044917 <NA>
## 7 D.P.http NA 1 NA <NA>
## 27 D.npnct02.log NA 0 NA <NA>
## 29 D.npnct04.log NA 0 NA <NA>
## 42 D.npnct17.log NA 0 NA <NA>
## 44 D.npnct19.log NA 0 NA <NA>
## 45 D.npnct20.log NA 0 NA <NA>
## 46 D.npnct21.log NA 0 NA <NA>
## 47 D.npnct22.log NA 0 NA <NA>
## 48 D.npnct23.log NA 0 NA <NA>
## 50 D.npnct25.log NA 0 NA <NA>
## 51 D.npnct26.log NA 0 NA <NA>
## 52 D.npnct27.log NA 0 NA <NA>
## 54 D.npnct29.log NA 0 NA <NA>
## 55 D.npnct30.log NA 0 NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 32 1858.0 0.10758472 FALSE TRUE TRUE FALSE
## 43 1858.0 0.10758472 FALSE TRUE TRUE FALSE
## 6 928.5 0.10758472 FALSE TRUE TRUE FALSE
## 7 0.0 0.05379236 TRUE TRUE TRUE NA
## 27 0.0 0.05379236 TRUE TRUE TRUE NA
## 29 0.0 0.05379236 TRUE TRUE TRUE NA
## 42 0.0 0.05379236 TRUE TRUE TRUE NA
## 44 0.0 0.05379236 TRUE TRUE TRUE NA
## 45 0.0 0.05379236 TRUE TRUE TRUE NA
## 46 0.0 0.05379236 TRUE TRUE TRUE NA
## 47 0.0 0.05379236 TRUE TRUE TRUE NA
## 48 0.0 0.05379236 TRUE TRUE TRUE NA
## 50 0.0 0.05379236 TRUE TRUE TRUE NA
## 51 0.0 0.05379236 TRUE TRUE TRUE NA
## 52 0.0 0.05379236 TRUE TRUE TRUE NA
## 54 0.0 0.05379236 TRUE TRUE TRUE NA
## 55 0.0 0.05379236 TRUE TRUE TRUE NAglb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold startprice.log
## 1444 999 31
## cellular.fctr D.terms.n.post.stop D.terms.n.post.stop.log
## 1600 1521 1521
## D.TfIdf.sum.post.stop D.terms.n.post.stem D.terms.n.post.stem.log
## 1521 1521 1521
## D.TfIdf.sum.post.stem D.T.condit D.T.use
## 1521 2161 2366
## D.T.scratch D.T.new D.T.good
## 2371 2501 2460
## D.T.ipad D.T.screen D.T.great
## 2425 2444 2532
## D.T.work D.T.excel D.nwrds.log
## 2459 2557 1520
## D.nwrds.unq.log D.sum.TfIdf D.ratio.sum.TfIdf.nwrds
## 1521 1521 1521
## D.nchrs.log D.nuppr.log D.ndgts.log
## 1520 1522 2427
## D.npnct01.log D.npnct03.log D.npnct05.log
## 2579 2614 2592
## D.npnct06.log D.npnct08.log D.npnct09.log
## 2554 2581 2641
## D.npnct10.log D.npnct11.log D.npnct12.log
## 2648 2301 2538
## D.npnct13.log D.npnct14.log D.npnct15.log
## 1932 2582 2637
## D.npnct16.log D.npnct24.log D.npnct28.log
## 2546 1520 2649
## D.nstopwrds.log D.P.mini D.P.air
## 1663 2623 2636
## D.P.black D.P.white D.P.spacegray
## 2640 2647 2650
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1520 0 0 0 0 0
## productline .grpid prdline.my descr.my
## 0 NA 0 1520# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 129.395 133.412 4.017
## 9 partition.data.training 6 0 133.412 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 524 450 NA
## OOB 475 410 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5379877 0.4620123 NA
## OOB 0.5367232 0.4632768 NA
## prdline.my .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 154 171 0.1929825 0.1932203
## 5 iPadAir 137 151 0.1716792 0.1706215
## 4 iPad 3+ 123 136 0.1541353 0.1536723
## 6 iPadmini 114 127 0.1428571 0.1435028
## 7 iPadmini 2+ 94 104 0.1177945 0.1175141
## 2 iPad 1 89 99 0.1115288 0.1118644
## 1 Unknown 87 97 0.1090226 0.1096045# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 81 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## 76 sold 1.0000000 TRUE 1.0000000 <NA>
## 68 UniqueID -0.1895466 TRUE 0.1895466 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV is.cor.y.abs.low
## 76 1.161628 0.1075847 FALSE FALSE FALSE FALSE
## 68 1.000000 100.0000000 FALSE FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA NA
## interaction.feat rsp_var_raw id_var rsp_var
## 76 <NA> TRUE NA NA
## 68 <NA> FALSE TRUE NA
## sold.fctr <NA> NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## [1] "D.npnct07.log" "D.npnct18.log" "D.P.gold" "D.P.http"
## [5] "D.npnct02.log" "D.npnct04.log" "D.npnct17.log" "D.npnct19.log"
## [9] "D.npnct20.log" "D.npnct21.log" "D.npnct22.log" "D.npnct23.log"
## [13] "D.npnct25.log" "D.npnct26.log" "D.npnct27.log" "D.npnct29.log"
## [17] "D.npnct30.log"print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2657 77print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1859 76print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 974 76print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 885 76print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 76# # Does not handle NULL or length(glb_id_var) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_trnobs_df[, glb_id_var],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_fitobs_df[, glb_id_var],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_OOBobs_df[, glb_id_var],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_var] %in% glb_newobs_df[, glb_id_var],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 133.412 134.301 0.889
## 10 fit.models 7 0 134.301 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(model_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5379877 0.4620123
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5379877 0.4620123
## 2 0.5379877 0.4620123
## 3 0.5379877 0.4620123
## 4 0.5379877 0.4620123
## 5 0.5379877 0.4620123
## 6 0.5379877 0.4620123
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.438 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.6320225
## 5 0.4 0.6320225
## 6 0.5 0.4747253
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 0 524
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.620123e-01 0.000000e+00 4.303445e-01 4.939104e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 9.999991e-01 1.552393e-115
## [1] " calling mypredict_mdl for OOB:"
## [1] "in Random.Classifier$prob"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.6332046
## 5 0.4 0.6332046
## 6 0.5 0.4822521
## 7 0.6 0.0000000
## 8 0.7 0.0000000
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 0 475
## Y 0 410
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.632768e-01 0.000000e+00 4.300283e-01 4.967706e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 9.999948e-01 7.120214e-105
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.253 0.001 0.5071756
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.6320225 0.4620123
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.4303445 0.4939104 0 0.5191913
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6332046 0.4632768
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4300283 0.4967706 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: biddable, startprice.diff"## Loading required package: rpart## Fitting cp = 0.511 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.5111111 0 1
##
## Node number 1: 974 observations
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.5379877 0.4620123) *
## [1] " calling mypredict_mdl for fit:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 524
## 2 Y 450
## Prediction
## Reference N Y
## N 524 0
## Y 450 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.379877e-01 0.000000e+00 5.060896e-01 5.696555e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.131410e-01 1.962860e-99
## [1] " calling mypredict_mdl for OOB:"
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 475
## 2 Y 410
## Prediction
## Reference N Y
## N 475 0
## Y 410 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.367232e-01 0.000000e+00 5.032294e-01 5.699717e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.137716e-01 9.975777e-91
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart biddable, startprice.diff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.61 0.012
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0 0.5379877
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.5060896 0.5696555 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.5367232
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.5032294 0.5699717 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: biddable, startprice.diff"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.086666667 1 0.4888889
## 3 0.002962963 2 0.4022222
## 4 0.002222222 5 0.3933333
## 5 0.001481481 18 0.3622222
## 6 0.001111111 21 0.3577778
## 7 0.000000000 23 0.3555556
##
## Variable importance
## biddable startprice.diff
## 59 41
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 59.64413 to the right, improve= 48.01938, (0 missing)
## Surrogate splits:
## startprice.diff < -57.54745 to the right, agree=0.548, adj=0.022, (0 split)
##
## Node number 2: 524 observations, complexity param=0.002222222
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
## left son=4 (246 obs) right son=5 (278 obs)
## Primary splits:
## startprice.diff < 25.55147 to the right, improve=8.564878, (0 missing)
##
## Node number 3: 450 observations, complexity param=0.08666667
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (135 obs) right son=7 (315 obs)
## Primary splits:
## startprice.diff < 68.91842 to the right, improve=61.71429, (0 missing)
##
## Node number 4: 246 observations
## predicted class=N expected loss=0.1138211 P(node) =0.2525667
## class counts: 218 28
## probabilities: 0.886 0.114
##
## Node number 5: 278 observations, complexity param=0.002222222
## predicted class=N expected loss=0.294964 P(node) =0.2854209
## class counts: 196 82
## probabilities: 0.705 0.295
## left son=10 (25 obs) right son=11 (253 obs)
## Primary splits:
## startprice.diff < -108.4389 to the left, improve=3.571512, (0 missing)
##
## Node number 6: 135 observations, complexity param=0.002962963
## predicted class=N expected loss=0.3555556 P(node) =0.1386037
## class counts: 87 48
## probabilities: 0.644 0.356
## left son=12 (81 obs) right son=13 (54 obs)
## Primary splits:
## startprice.diff < 113.3966 to the right, improve=2.854321, (0 missing)
##
## Node number 7: 315 observations
## predicted class=Y expected loss=0.07301587 P(node) =0.3234086
## class counts: 23 292
## probabilities: 0.073 0.927
##
## Node number 10: 25 observations
## predicted class=N expected loss=0.04 P(node) =0.02566735
## class counts: 24 1
## probabilities: 0.960 0.040
##
## Node number 11: 253 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3201581 P(node) =0.2597536
## class counts: 172 81
## probabilities: 0.680 0.320
## left son=22 (91 obs) right son=23 (162 obs)
## Primary splits:
## startprice.diff < -2.694213 to the right, improve=1.747059, (0 missing)
##
## Node number 12: 81 observations
## predicted class=N expected loss=0.2716049 P(node) =0.08316222
## class counts: 59 22
## probabilities: 0.728 0.272
##
## Node number 13: 54 observations, complexity param=0.002962963
## predicted class=N expected loss=0.4814815 P(node) =0.05544148
## class counts: 28 26
## probabilities: 0.519 0.481
## left son=26 (20 obs) right son=27 (34 obs)
## Primary splits:
## startprice.diff < 95.05288 to the right, improve=0.4217865, (0 missing)
##
## Node number 22: 91 observations, complexity param=0.001111111
## predicted class=N expected loss=0.2417582 P(node) =0.09342916
## class counts: 69 22
## probabilities: 0.758 0.242
## left son=44 (45 obs) right son=45 (46 obs)
## Primary splits:
## startprice.diff < 10.66358 to the left, improve=1.323024, (0 missing)
##
## Node number 23: 162 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3641975 P(node) =0.1663244
## class counts: 103 59
## probabilities: 0.636 0.364
## left son=46 (51 obs) right son=47 (111 obs)
## Primary splits:
## startprice.diff < -41.27746 to the left, improve=0.7311037, (0 missing)
##
## Node number 26: 20 observations, complexity param=0.002222222
## predicted class=N expected loss=0.4 P(node) =0.02053388
## class counts: 12 8
## probabilities: 0.600 0.400
## left son=52 (11 obs) right son=53 (9 obs)
## Primary splits:
## startprice.diff < 102.2475 to the left, improve=0.7919192, (0 missing)
##
## Node number 27: 34 observations, complexity param=0.002962963
## predicted class=Y expected loss=0.4705882 P(node) =0.0349076
## class counts: 16 18
## probabilities: 0.471 0.529
## left son=54 (8 obs) right son=55 (26 obs)
## Primary splits:
## startprice.diff < 75.75335 to the left, improve=0.4988688, (0 missing)
##
## Node number 44: 45 observations
## predicted class=N expected loss=0.1555556 P(node) =0.04620123
## class counts: 38 7
## probabilities: 0.844 0.156
##
## Node number 45: 46 observations, complexity param=0.001111111
## predicted class=N expected loss=0.326087 P(node) =0.04722793
## class counts: 31 15
## probabilities: 0.674 0.326
## left son=90 (39 obs) right son=91 (7 obs)
## Primary splits:
## startprice.diff < 12.77658 to the right, improve=0.9939481, (0 missing)
##
## Node number 46: 51 observations, complexity param=0.001481481
## predicted class=N expected loss=0.2941176 P(node) =0.0523614
## class counts: 36 15
## probabilities: 0.706 0.294
## left son=92 (7 obs) right son=93 (44 obs)
## Primary splits:
## startprice.diff < -45.97369 to the right, improve=1.403743, (0 missing)
##
## Node number 47: 111 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3963964 P(node) =0.113963
## class counts: 67 44
## probabilities: 0.604 0.396
## left son=94 (71 obs) right son=95 (40 obs)
## Primary splits:
## startprice.diff < -28.12784 to the right, improve=2.068526, (0 missing)
##
## Node number 52: 11 observations
## predicted class=N expected loss=0.2727273 P(node) =0.01129363
## class counts: 8 3
## probabilities: 0.727 0.273
##
## Node number 53: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.009240246
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 54: 8 observations
## predicted class=N expected loss=0.375 P(node) =0.008213552
## class counts: 5 3
## probabilities: 0.625 0.375
##
## Node number 55: 26 observations
## predicted class=Y expected loss=0.4230769 P(node) =0.02669405
## class counts: 11 15
## probabilities: 0.423 0.577
##
## Node number 90: 39 observations
## predicted class=N expected loss=0.2820513 P(node) =0.04004107
## class counts: 28 11
## probabilities: 0.718 0.282
##
## Node number 91: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007186858
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 92: 7 observations
## predicted class=N expected loss=0 P(node) =0.007186858
## class counts: 7 0
## probabilities: 1.000 0.000
##
## Node number 93: 44 observations, complexity param=0.001481481
## predicted class=N expected loss=0.3409091 P(node) =0.04517454
## class counts: 29 15
## probabilities: 0.659 0.341
## left son=186 (20 obs) right son=187 (24 obs)
## Primary splits:
## startprice.diff < -61.79786 to the right, improve=0.6060606, (0 missing)
##
## Node number 94: 71 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3239437 P(node) =0.07289528
## class counts: 48 23
## probabilities: 0.676 0.324
## left son=188 (10 obs) right son=189 (61 obs)
## Primary splits:
## startprice.diff < -25.00492 to the left, improve=2.442854, (0 missing)
##
## Node number 95: 40 observations, complexity param=0.002222222
## predicted class=Y expected loss=0.475 P(node) =0.04106776
## class counts: 19 21
## probabilities: 0.475 0.525
## left son=190 (30 obs) right son=191 (10 obs)
## Primary splits:
## startprice.diff < -31.52075 to the left, improve=0.8166667, (0 missing)
##
## Node number 186: 20 observations
## predicted class=N expected loss=0.25 P(node) =0.02053388
## class counts: 15 5
## probabilities: 0.750 0.250
##
## Node number 187: 24 observations, complexity param=0.001481481
## predicted class=N expected loss=0.4166667 P(node) =0.02464066
## class counts: 14 10
## probabilities: 0.583 0.417
## left son=374 (16 obs) right son=375 (8 obs)
## Primary splits:
## startprice.diff < -72.02612 to the left, improve=1.041667, (0 missing)
##
## Node number 188: 10 observations
## predicted class=N expected loss=0 P(node) =0.01026694
## class counts: 10 0
## probabilities: 1.000 0.000
##
## Node number 189: 61 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3770492 P(node) =0.06262834
## class counts: 38 23
## probabilities: 0.623 0.377
## left son=378 (54 obs) right son=379 (7 obs)
## Primary splits:
## startprice.diff < -21.36215 to the right, improve=0.5975366, (0 missing)
##
## Node number 190: 30 observations, complexity param=0.002222222
## predicted class=N expected loss=0.4666667 P(node) =0.03080082
## class counts: 16 14
## probabilities: 0.533 0.467
## left son=380 (7 obs) right son=381 (23 obs)
## Primary splits:
## startprice.diff < -34.25742 to the right, improve=0.5979296, (0 missing)
##
## Node number 191: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.01026694
## class counts: 3 7
## probabilities: 0.300 0.700
##
## Node number 374: 16 observations
## predicted class=N expected loss=0.3125 P(node) =0.0164271
## class counts: 11 5
## probabilities: 0.688 0.312
##
## Node number 375: 8 observations
## predicted class=Y expected loss=0.375 P(node) =0.008213552
## class counts: 3 5
## probabilities: 0.375 0.625
##
## Node number 378: 54 observations, complexity param=0.002222222
## predicted class=N expected loss=0.3518519 P(node) =0.05544148
## class counts: 35 19
## probabilities: 0.648 0.352
## left son=756 (17 obs) right son=757 (37 obs)
## Primary splits:
## startprice.diff < -13.74554 to the left, improve=1.526291, (0 missing)
##
## Node number 379: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.007186858
## class counts: 3 4
## probabilities: 0.429 0.571
##
## Node number 380: 7 observations
## predicted class=N expected loss=0.2857143 P(node) =0.007186858
## class counts: 5 2
## probabilities: 0.714 0.286
##
## Node number 381: 23 observations, complexity param=0.002222222
## predicted class=Y expected loss=0.4782609 P(node) =0.02361396
## class counts: 11 12
## probabilities: 0.478 0.522
## left son=762 (15 obs) right son=763 (8 obs)
## Primary splits:
## startprice.diff < -36.76706 to the left, improve=1.278261, (0 missing)
##
## Node number 756: 17 observations
## predicted class=N expected loss=0.1764706 P(node) =0.0174538
## class counts: 14 3
## probabilities: 0.824 0.176
##
## Node number 757: 37 observations, complexity param=0.002222222
## predicted class=N expected loss=0.4324324 P(node) =0.03798768
## class counts: 21 16
## probabilities: 0.568 0.432
## left son=1514 (29 obs) right son=1515 (8 obs)
## Primary splits:
## startprice.diff < -11.7343 to the right, improve=2.058714, (0 missing)
##
## Node number 762: 15 observations
## predicted class=N expected loss=0.4 P(node) =0.01540041
## class counts: 9 6
## probabilities: 0.600 0.400
##
## Node number 763: 8 observations
## predicted class=Y expected loss=0.25 P(node) =0.008213552
## class counts: 2 6
## probabilities: 0.250 0.750
##
## Node number 1514: 29 observations
## predicted class=N expected loss=0.3448276 P(node) =0.02977413
## class counts: 19 10
## probabilities: 0.655 0.345
##
## Node number 1515: 8 observations
## predicted class=Y expected loss=0.25 P(node) =0.008213552
## class counts: 2 6
## probabilities: 0.250 0.750
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366)
## 4) startprice.diff>=25.55147 246 28 N (0.88617886 0.11382114) *
## 5) startprice.diff< 25.55147 278 82 N (0.70503597 0.29496403)
## 10) startprice.diff< -108.4389 25 1 N (0.96000000 0.04000000) *
## 11) startprice.diff>=-108.4389 253 81 N (0.67984190 0.32015810)
## 22) startprice.diff>=-2.694213 91 22 N (0.75824176 0.24175824)
## 44) startprice.diff< 10.66358 45 7 N (0.84444444 0.15555556) *
## 45) startprice.diff>=10.66358 46 15 N (0.67391304 0.32608696)
## 90) startprice.diff>=12.77658 39 11 N (0.71794872 0.28205128) *
## 91) startprice.diff< 12.77658 7 3 Y (0.42857143 0.57142857) *
## 23) startprice.diff< -2.694213 162 59 N (0.63580247 0.36419753)
## 46) startprice.diff< -41.27746 51 15 N (0.70588235 0.29411765)
## 92) startprice.diff>=-45.97369 7 0 N (1.00000000 0.00000000) *
## 93) startprice.diff< -45.97369 44 15 N (0.65909091 0.34090909)
## 186) startprice.diff>=-61.79786 20 5 N (0.75000000 0.25000000) *
## 187) startprice.diff< -61.79786 24 10 N (0.58333333 0.41666667)
## 374) startprice.diff< -72.02612 16 5 N (0.68750000 0.31250000) *
## 375) startprice.diff>=-72.02612 8 3 Y (0.37500000 0.62500000) *
## 47) startprice.diff>=-41.27746 111 44 N (0.60360360 0.39639640)
## 94) startprice.diff>=-28.12784 71 23 N (0.67605634 0.32394366)
## 188) startprice.diff< -25.00492 10 0 N (1.00000000 0.00000000) *
## 189) startprice.diff>=-25.00492 61 23 N (0.62295082 0.37704918)
## 378) startprice.diff>=-21.36215 54 19 N (0.64814815 0.35185185)
## 756) startprice.diff< -13.74554 17 3 N (0.82352941 0.17647059) *
## 757) startprice.diff>=-13.74554 37 16 N (0.56756757 0.43243243)
## 1514) startprice.diff>=-11.7343 29 10 N (0.65517241 0.34482759) *
## 1515) startprice.diff< -11.7343 8 2 Y (0.25000000 0.75000000) *
## 379) startprice.diff< -21.36215 7 3 Y (0.42857143 0.57142857) *
## 95) startprice.diff< -28.12784 40 19 Y (0.47500000 0.52500000)
## 190) startprice.diff< -31.52075 30 14 N (0.53333333 0.46666667)
## 380) startprice.diff>=-34.25742 7 2 N (0.71428571 0.28571429) *
## 381) startprice.diff< -34.25742 23 11 Y (0.47826087 0.52173913)
## 762) startprice.diff< -36.76706 15 6 N (0.60000000 0.40000000) *
## 763) startprice.diff>=-36.76706 8 2 Y (0.25000000 0.75000000) *
## 191) startprice.diff>=-31.52075 10 3 Y (0.30000000 0.70000000) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=68.91842 135 48 N (0.64444444 0.35555556)
## 12) startprice.diff>=113.3966 81 22 N (0.72839506 0.27160494) *
## 13) startprice.diff< 113.3966 54 26 N (0.51851852 0.48148148)
## 26) startprice.diff>=95.05288 20 8 N (0.60000000 0.40000000)
## 52) startprice.diff< 102.2475 11 3 N (0.72727273 0.27272727) *
## 53) startprice.diff>=102.2475 9 4 Y (0.44444444 0.55555556) *
## 27) startprice.diff< 95.05288 34 16 Y (0.47058824 0.52941176)
## 54) startprice.diff< 75.75335 8 3 N (0.62500000 0.37500000) *
## 55) startprice.diff>=75.75335 26 11 Y (0.42307692 0.57692308) *
## 7) startprice.diff< 68.91842 315 23 Y (0.07301587 0.92698413) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6497829
## 3 0.2 0.7653631
## 4 0.3 0.8034934
## 5 0.4 0.8111240
## 6 0.5 0.8113208
## 7 0.6 0.7909887
## 8 0.7 0.7784891
## 9 0.8 0.7633987
## 10 0.9 0.7633987
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 470
## 2 Y 106
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 54
## 2 344
## Prediction
## Reference N Y
## N 470 54
## Y 106 344
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.357290e-01 6.668320e-01 8.109392e-01 8.584688e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 6.197689e-86 5.532678e-05
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6254019
## 3 0.2 0.7198321
## 4 0.3 0.7467301
## 5 0.4 0.7471410
## 6 0.5 0.7551546
## 7 0.6 0.7349727
## 8 0.7 0.7357955
## 9 0.8 0.7420290
## 10 0.9 0.7420290
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 402
## 2 Y 117
## sold.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 73
## 2 293
## Prediction
## Reference N Y
## N 402 73
## Y 117 293
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.853107e-01 5.650993e-01 7.567658e-01 8.119416e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.847896e-53 1.811288e-03
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart biddable, startprice.diff
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.488 0.009
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8855386 0.5 0.8113208 0.835729
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8109392 0.8584688 0.666832 0.8187625
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7551546 0.7853107
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7567658 0.8119416 0.5650993if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable, startprice.diff"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00296 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.086666667 1 0.4888889
## 3 0.002962963 2 0.4022222
##
## Variable importance
## biddable startprice.diff
## 69 31
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 59.64413 to the right, improve= 48.01938, (0 missing)
## Surrogate splits:
## startprice.diff < -57.54745 to the right, agree=0.548, adj=0.022, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.08666667
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (135 obs) right son=7 (315 obs)
## Primary splits:
## startprice.diff < 68.91842 to the right, improve=61.71429, (0 missing)
##
## Node number 6: 135 observations
## predicted class=N expected loss=0.3555556 P(node) =0.1386037
## class counts: 87 48
## probabilities: 0.644 0.356
##
## Node number 7: 315 observations
## predicted class=Y expected loss=0.07301587 P(node) =0.3234086
## class counts: 23 292
## probabilities: 0.073 0.927
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=68.91842 135 48 N (0.64444444 0.35555556) *
## 7) startprice.diff< 68.91842 315 23 Y (0.07301587 0.92698413) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7555556
## 5 0.4 0.7633987
## 6 0.5 0.7633987
## 7 0.6 0.7633987
## 8 0.7 0.7633987
## 9 0.8 0.7633987
## 10 0.9 0.7633987
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 501
## 2 Y 158
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 23
## 2 292
## Prediction
## Reference N Y
## N 501 23
## Y 158 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.141684e-01 6.180889e-01 7.882906e-01 8.381305e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.530340e-73 2.277382e-23
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7528231
## 5 0.4 0.7420290
## 6 0.5 0.7420290
## 7 0.6 0.7420290
## 8 0.7 0.7420290
## 9 0.8 0.7420290
## 10 0.9 0.7420290
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 388
## 2 Y 110
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 87
## 2 300
## Prediction
## Reference N Y
## N 388 87
## Y 110 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.774011e-01 5.506630e-01 7.485294e-01 8.044124e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.956102e-50 1.170130e-01
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart biddable, startprice.diff 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.072 0.012 0.8243427
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.7633987 0.7833523
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7882906 0.8381305 0.5585199 0.8129705
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7528231 0.7774011
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7485294 0.8044124 0.550663
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01779075 0.03354379# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.diff"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4575 -0.7149 -0.3051 0.6402 2.7683
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1939636 0.1132804 -10.540 <2e-16 ***
## biddable 2.8629786 0.1784163 16.047 <2e-16 ***
## startprice.diff -0.0095589 0.0009883 -9.672 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 913.48 on 971 degrees of freedom
## AIC: 919.48
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6697178
## 3 0.2 0.7222222
## 4 0.3 0.7387755
## 5 0.4 0.7444934
## 6 0.5 0.7514188
## 7 0.6 0.7639383
## 8 0.7 0.7639594
## 9 0.8 0.6637807
## 10 0.9 0.2373541
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 487
## 2 Y 149
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 37
## 2 301
## Prediction
## Reference N Y
## N 487 37
## Y 149 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.090349e-01 6.089806e-01 7.829169e-01 8.332690e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.451542e-70 3.988362e-16
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6705882
## 3 0.2 0.7213740
## 4 0.3 0.7474519
## 5 0.4 0.7601476
## 6 0.5 0.7668790
## 7 0.6 0.7710526
## 8 0.7 0.7580420
## 9 0.8 0.6864275
## 10 0.9 0.2547771
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Max.cor.Y.glm.N
## 1 N 418
## 2 Y 117
## sold.fctr.predict.Max.cor.Y.glm.Y
## 1 57
## 2 293
## Prediction
## Reference N Y
## N 418 57
## Y 117 293
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.033898e-01 6.006483e-01 7.756483e-01 8.290941e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 7.775424e-62 7.720976e-06
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm biddable, startprice.diff 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.004 0.011 0.8586302
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.7639594 0.7720798
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7829169 0.833269 0.5402037 0.8633582
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7710526 0.8033898
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7756483 0.8290941 0.6006483 919.4841
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.005815317 0.0112495if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.glm"
## [1] " indep_vars: biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.npnct24.log, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:D.terms.n.post.stop.log, biddable:cellular.fctr, biddable:D.nwrds.unq.log"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3815 -0.7141 -0.3046 0.6147 2.7639
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1938518 0.1132307 -10.544 <2e-16
## biddable -0.1443348 3.7603826 -0.038 0.9694
## startprice.diff -0.0095134 0.0009984 -9.528 <2e-16
## `biddable:D.terms.n.post.stop` -0.2126668 0.3174709 -0.670 0.5029
## `biddable:D.TfIdf.sum.post.stem` 0.1714728 0.2046400 0.838 0.4021
## `biddable:D.npnct24.log` -3.0403645 3.9688018 -0.766 0.4436
## `biddable:D.npnct06.log` 0.2116318 0.9764780 0.217 0.8284
## `biddable:D.ratio.nstopwrds.nwrds` 3.1336058 3.7549448 0.835 0.4040
## `biddable:D.nchrs.log` 1.6977216 1.6433973 1.033 0.3016
## `biddable:D.nwrds.log` -3.6103019 2.6293833 -1.373 0.1697
## `biddable:D.terms.n.post.stop.log` 9.4879364 8.5423492 1.111 0.2667
## `biddable:cellular.fctr1` -0.0077050 0.2971903 -0.026 0.9793
## `biddable:cellular.fctrUnknown` -0.8918723 0.3598666 -2.478 0.0132
## `biddable:D.nwrds.unq.log` -6.3624298 7.2654801 -0.876 0.3812
##
## (Intercept) ***
## biddable
## startprice.diff ***
## `biddable:D.terms.n.post.stop`
## `biddable:D.TfIdf.sum.post.stem`
## `biddable:D.npnct24.log`
## `biddable:D.npnct06.log`
## `biddable:D.ratio.nstopwrds.nwrds`
## `biddable:D.nchrs.log`
## `biddable:D.nwrds.log`
## `biddable:D.terms.n.post.stop.log`
## `biddable:cellular.fctr1`
## `biddable:cellular.fctrUnknown` *
## `biddable:D.nwrds.unq.log`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 903.82 on 960 degrees of freedom
## AIC: 931.82
##
## Number of Fisher Scoring iterations: 5
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6692073
## 3 0.2 0.7211121
## 4 0.3 0.7379239
## 5 0.4 0.7439294
## 6 0.5 0.7542857
## 7 0.6 0.7592814
## 8 0.7 0.7615385
## 9 0.8 0.6827881
## 10 0.9 0.3097015
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 491
## 2 Y 153
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 33
## 2 297
## Prediction
## Reference N Y
## N 491 33
## Y 153 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.090349e-01 6.084803e-01 7.829169e-01 8.332690e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 2.451542e-70 2.649993e-18
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6717045
## 3 0.2 0.7206864
## 4 0.3 0.7491487
## 5 0.4 0.7570900
## 6 0.5 0.7634961
## 7 0.6 0.7665782
## 8 0.7 0.7566064
## 9 0.8 0.6883721
## 10 0.9 0.3108384
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glm.N
## 1 N 420
## 2 Y 121
## sold.fctr.predict.Interact.High.cor.Y.glm.Y
## 1 55
## 2 289
## Prediction
## Reference N Y
## N 420 55
## Y 121 289
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.011299e-01 5.956491e-01 7.732835e-01 8.269546e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 9.548473e-61 9.605183e-07
## model_id model_method
## 1 Interact.High.cor.Y.glm glm
## feats
## 1 biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.npnct24.log, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:D.terms.n.post.stop.log, biddable:cellular.fctr, biddable:D.nwrds.unq.log
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.047 0.019
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8580831 0.7 0.7615385 0.7741374
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7829169 0.833269 0.5436041 0.8615815
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7665782 0.8011299
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7732835 0.8269546 0.5956491 931.8178
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008498508 0.01820715# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, color.fctr, D.npnct08.log, prdline.my.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4418 -0.6549 -0.1206 0.5712 2.7843
##
## Coefficients: (14 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 6.341e+00 5.579e+00
## biddable 3.167e+00 2.290e-01
## D.npnct15.log 1.820e+00 8.444e-01
## D.npnct03.log 8.152e-01 1.618e+00
## D.terms.n.stem.stop.Ratio -6.327e+00 4.970e+00
## D.ratio.sum.TfIdf.nwrds 1.417e-01 1.530e-01
## .rnorm 5.538e-02 9.453e-02
## D.npnct01.log 4.225e-01 5.459e-01
## D.TfIdf.sum.stem.stop.Ratio -1.242e+00 2.937e+00
## storage.fctr16 -4.534e-01 5.152e-01
## storage.fctr32 -6.375e-01 5.447e-01
## storage.fctr64 -4.850e-02 5.365e-01
## storage.fctrUnknown -8.709e-01 7.060e-01
## D.npnct11.log 1.296e-01 3.429e-01
## D.npnct10.log -2.338e+01 1.317e+03
## color.fctrGold 2.765e-02 5.692e-01
## `color.fctrSpace Gray` -4.946e-01 3.704e-01
## color.fctrUnknown -2.570e-02 2.526e-01
## color.fctrWhite -3.653e-01 2.752e-01
## D.npnct08.log 4.442e-01 6.743e-01
## `prdline.my.fctriPad 1` 9.490e-01 5.605e-01
## `prdline.my.fctriPad 2` 4.461e-01 5.666e-01
## `prdline.my.fctriPad 3+` 8.958e-01 5.453e-01
## prdline.my.fctriPadAir 1.222e+00 5.453e-01
## prdline.my.fctriPadmini 6.066e-01 5.270e-01
## `prdline.my.fctriPadmini 2+` 9.005e-01 5.764e-01
## D.npnct06.log -2.440e+00 1.169e+00
## D.npnct28.log -3.104e+00 1.723e+03
## D.npnct12.log 1.543e-01 6.726e-01
## D.npnct09.log -8.833e+00 7.853e+02
## D.ndgts.log 7.185e-01 4.302e-01
## cellular.fctr1 1.579e-01 2.217e-01
## cellular.fctrUnknown -3.665e-01 4.801e-01
## D.npnct14.log -2.104e+00 1.071e+00
## D.terms.n.post.stop -8.192e-02 4.721e-02
## D.npnct05.log -3.242e+00 1.650e+00
## `condition.fctrFor parts or not working` 1.365e-01 3.602e-01
## `condition.fctrManufacturer refurbished` 6.356e-01 6.277e-01
## condition.fctrNew -2.826e-01 3.140e-01
## `condition.fctrNew other (see details)` 6.201e-02 4.745e-01
## `condition.fctrSeller refurbished` -3.296e-01 4.284e-01
## idseq.my -1.667e-04 2.054e-04
## startprice.diff -1.151e-02 1.298e-03
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.958e+00 7.051e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 9.845e-01 7.872e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 8.304e-01 6.627e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -3.736e-02 6.866e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -2.976e-01 6.431e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.347e+00 7.246e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -4.480e-01 9.284e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.029e-01 9.268e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 2.594e-01 8.937e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -6.578e-01 1.262e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.699e-01 8.631e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -5.099e-01 8.164e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.144e+00 8.423e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 3.291e-01 9.275e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.464e+00 8.445e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 3.576e+00 1.430e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 8.138e-01 8.477e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -2.246e-01 8.900e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -5.189e-01 8.338e-01
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -6.973e-01 1.234e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.430e+01 9.056e+02
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA NA
## z value Pr(>|z|)
## (Intercept) 1.137 0.25568
## biddable 13.827 < 2e-16 ***
## D.npnct15.log 2.155 0.03118 *
## D.npnct03.log 0.504 0.61445
## D.terms.n.stem.stop.Ratio -1.273 0.20301
## D.ratio.sum.TfIdf.nwrds 0.926 0.35452
## .rnorm 0.586 0.55799
## D.npnct01.log 0.774 0.43901
## D.TfIdf.sum.stem.stop.Ratio -0.423 0.67241
## storage.fctr16 -0.880 0.37885
## storage.fctr32 -1.170 0.24187
## storage.fctr64 -0.090 0.92797
## storage.fctrUnknown -1.234 0.21733
## D.npnct11.log 0.378 0.70534
## D.npnct10.log -0.018 0.98584
## color.fctrGold 0.049 0.96126
## `color.fctrSpace Gray` -1.335 0.18178
## color.fctrUnknown -0.102 0.91896
## color.fctrWhite -1.327 0.18445
## D.npnct08.log 0.659 0.51004
## `prdline.my.fctriPad 1` 1.693 0.09041 .
## `prdline.my.fctriPad 2` 0.787 0.43112
## `prdline.my.fctriPad 3+` 1.643 0.10040
## prdline.my.fctriPadAir 2.241 0.02504 *
## prdline.my.fctriPadmini 1.151 0.24978
## `prdline.my.fctriPadmini 2+` 1.562 0.11821
## D.npnct06.log -2.088 0.03684 *
## D.npnct28.log -0.002 0.99856
## D.npnct12.log 0.229 0.81852
## D.npnct09.log -0.011 0.99103
## D.ndgts.log 1.670 0.09491 .
## cellular.fctr1 0.712 0.47626
## cellular.fctrUnknown -0.763 0.44525
## D.npnct14.log -1.964 0.04952 *
## D.terms.n.post.stop -1.735 0.08268 .
## D.npnct05.log -1.964 0.04952 *
## `condition.fctrFor parts or not working` 0.379 0.70475
## `condition.fctrManufacturer refurbished` 1.013 0.31126
## condition.fctrNew -0.900 0.36812
## `condition.fctrNew other (see details)` 0.131 0.89602
## `condition.fctrSeller refurbished` -0.769 0.44167
## idseq.my -0.812 0.41707
## startprice.diff -8.869 < 2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.777 0.00549 **
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.251 0.21111
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.253 0.21020
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.054 0.95661
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.463 0.64349
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.859 0.06307 .
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.483 0.62944
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.111 0.91160
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.290 0.77165
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.521 0.60232
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.197 0.84398
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.625 0.53228
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.359 0.17426
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.355 0.72267
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.734 0.08299 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.501 0.01240 *
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.960 0.33707
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.252 0.80077
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -0.622 0.53371
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.565 0.57217
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.016 0.98740
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 810.35 on 910 degrees of freedom
## AIC: 938.35
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7017828
## 3 0.2 0.7534122
## 4 0.3 0.7613412
## 5 0.4 0.7781350
## 6 0.5 0.7977143
## 7 0.6 0.7951807
## 8 0.7 0.7827192
## 9 0.8 0.7019499
## 10 0.9 0.4808013
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 448
## 2 Y 101
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 76
## 2 349
## Prediction
## Reference N Y
## N 448 76
## Y 101 349
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.182752e-01 6.330005e-01 7.925945e-01 8.420145e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.666328e-75 7.123907e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6948695
## 3 0.2 0.7241379
## 4 0.3 0.7451869
## 5 0.4 0.7615572
## 6 0.5 0.7733675
## 7 0.6 0.7680000
## 8 0.7 0.7493036
## 9 0.8 0.6687023
## 10 0.9 0.4810127
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.Low.cor.X.glm.N
## 1 N 406
## 2 Y 108
## sold.fctr.predict.Low.cor.X.glm.Y
## 1 69
## 2 302
## Prediction
## Reference N Y
## N 406 69
## Y 108 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.000000e-01 5.951959e-01 7.721016e-01 8.258843e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.310805e-60 4.286708e-03
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, color.fctr, D.npnct08.log, prdline.my.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.49 0.182
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8858185 0.5 0.7977143 0.771032
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7925945 0.8420145 0.5386776 0.8465571
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7733675 0.8
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7721016 0.8258843 0.5951959 938.3505
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01583674 0.03271193rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 134.301 161.263 26.962
## 11 fit.models 7 1 161.264 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 165.679 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
for (model_id_pfx in c("All.X", "All.Interact.X")) {
#model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
if (model_id_pfx == "All.Interact.X") {
# !_sp
interact_vars_vctr <- c(
"idseq.my", "D.ratio.sum.TfIdf.nwrds", "D.TfIdf.sum.stem.stop.Ratio",
"D.npnct15.log", "D.npnct03.log")
###
# _sp only
# interact_vars_vctr <- c(
# "D.nchrs.log", "D.TfIdf.sum.stem.stop.Ratio", "D.npnct16.log", "D.npnct08.log",
# "biddable", "condition.fctr",
# # "cellular.fctr", "carrier.fctr",
# "color.fctr", "storage.fctr", "idseq.my")
###
indep_vars_vctr <- union(setdiff(indep_vars_vctr, interact_vars_vctr),
paste(glb_category_var, interact_vars_vctr, sep=".fctr*"))
indep_vars_vctr <- union(setdiff(indep_vars_vctr,
c("startprice.diff", "biddable", "cellular.fctr", "carrier.fctr")),
c("startprice.diff*biddable", "cellular.fctr*carrier.fctr"))
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
#stop(here")
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# mydsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 165.679 165.688 0.009
## 2 fit.models_1_glm 2 0 165.688 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.63237 -0.62729 -0.09729 0.53513 2.86973
##
## Coefficients: (17 not defined because of singularities)
## Estimate Std. Error
## (Intercept) -3.417e+03 2.382e+03
## biddable 3.285e+00 2.399e-01
## D.ratio.nstopwrds.nwrds -1.522e+01 7.020e+00
## D.npnct15.log 1.485e+00 9.508e-01
## D.npnct03.log 1.329e+00 1.950e+00
## D.terms.n.stem.stop.Ratio 3.435e+03 2.383e+03
## D.ratio.sum.TfIdf.nwrds 8.880e-03 6.058e-01
## .rnorm 4.776e-02 9.762e-02
## D.npnct01.log 3.970e-01 6.511e-01
## D.TfIdf.sum.stem.stop.Ratio -3.781e+00 1.791e+01
## storage.fctr16 -5.194e-01 5.209e-01
## storage.fctr32 -6.846e-01 5.508e-01
## storage.fctr64 -4.977e-02 5.412e-01
## storage.fctrUnknown -1.002e+00 7.154e-01
## D.npnct11.log 2.059e-01 3.802e-01
## D.npnct10.log -2.486e+01 1.214e+03
## D.TfIdf.sum.post.stop -3.669e-01 2.671e+00
## D.npnct13.log 1.931e-01 4.037e-01
## D.TfIdf.sum.post.stem 6.515e-01 2.783e+00
## D.sum.TfIdf NA NA
## color.fctrGold 6.483e-02 5.831e-01
## `color.fctrSpace Gray` -4.747e-01 3.835e-01
## color.fctrUnknown -6.167e-02 2.623e-01
## color.fctrWhite -3.142e-01 2.843e-01
## D.npnct08.log 6.921e-01 7.478e-01
## `prdline.my.fctriPad 1` 1.017e+00 5.746e-01
## `prdline.my.fctriPad 2` 3.617e-01 5.799e-01
## `prdline.my.fctriPad 3+` 8.072e-01 5.568e-01
## prdline.my.fctriPadAir 1.173e+00 5.584e-01
## prdline.my.fctriPadmini 5.929e-01 5.430e-01
## `prdline.my.fctriPadmini 2+` 8.992e-01 5.880e-01
## D.nstopwrds.log 3.840e+00 2.000e+00
## D.npnct16.log 2.863e+00 1.855e+00
## D.npnct24.log -5.010e+00 5.873e+00
## D.npnct06.log -5.705e+00 2.272e+00
## D.npnct28.log -4.265e+00 1.736e+03
## D.nuppr.log 2.134e+00 3.144e+00
## D.nchrs.log -2.806e+00 3.687e+00
## D.nwrds.log -2.461e-01 3.126e+00
## D.npnct12.log 6.252e-01 7.751e-01
## carrier.fctrNone 2.240e-01 7.249e-01
## carrier.fctrOther 1.395e+01 1.624e+03
## carrier.fctrSprint 1.253e+00 8.097e-01
## `carrier.fctrT-Mobile` -7.507e-01 1.035e+00
## carrier.fctrUnknown -1.024e-01 5.273e-01
## carrier.fctrVerizon 5.040e-01 4.853e-01
## D.npnct09.log -7.855e+00 8.278e+02
## D.ndgts.log 5.038e-01 5.452e-01
## D.nwrds.unq.log -3.887e+03 2.673e+03
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 3.883e+03 2.673e+03
## cellular.fctr1 2.771e-01 6.622e-01
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.544e+00 1.176e+00
## D.terms.n.post.stem 2.639e+01 1.661e+01
## D.terms.n.post.stop -2.642e+01 1.656e+01
## D.npnct05.log -2.640e+00 1.799e+00
## `condition.fctrFor parts or not working` -2.919e-02 3.814e-01
## `condition.fctrManufacturer refurbished` 5.394e-01 6.406e-01
## condition.fctrNew -2.353e-01 3.242e-01
## `condition.fctrNew other (see details)` -6.686e-02 5.029e-01
## `condition.fctrSeller refurbished` -3.336e-01 4.441e-01
## idseq.my -1.515e-04 2.109e-04
## startprice.diff -1.203e-02 1.366e-03
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.768e+00 7.792e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 7.825e-01 8.591e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 8.548e-01 7.425e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 1.112e-01 7.261e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.294e-01 7.044e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.051e+00 7.692e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -5.710e-01 1.010e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.765e-01 1.041e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.338e-01 9.369e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -3.401e-01 1.311e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -1.060e-01 9.774e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -8.818e-01 9.882e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.058e+00 9.597e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -8.029e-02 1.006e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.477e+00 9.059e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 3.114e+00 1.432e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 7.984e-01 9.712e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.965e-01 9.260e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -1.295e+00 9.826e-01
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -1.029e+00 1.377e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.443e+01 9.181e+02
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA NA
## z value Pr(>|z|)
## (Intercept) -1.435 0.1514
## biddable 13.694 <2e-16 ***
## D.ratio.nstopwrds.nwrds -2.168 0.0301 *
## D.npnct15.log 1.562 0.1182
## D.npnct03.log 0.682 0.4954
## D.terms.n.stem.stop.Ratio 1.441 0.1495
## D.ratio.sum.TfIdf.nwrds 0.015 0.9883
## .rnorm 0.489 0.6247
## D.npnct01.log 0.610 0.5420
## D.TfIdf.sum.stem.stop.Ratio -0.211 0.8328
## storage.fctr16 -0.997 0.3187
## storage.fctr32 -1.243 0.2139
## storage.fctr64 -0.092 0.9267
## storage.fctrUnknown -1.401 0.1612
## D.npnct11.log 0.542 0.5881
## D.npnct10.log -0.020 0.9837
## D.TfIdf.sum.post.stop -0.137 0.8907
## D.npnct13.log 0.478 0.6324
## D.TfIdf.sum.post.stem 0.234 0.8149
## D.sum.TfIdf NA NA
## color.fctrGold 0.111 0.9115
## `color.fctrSpace Gray` -1.238 0.2158
## color.fctrUnknown -0.235 0.8141
## color.fctrWhite -1.105 0.2690
## D.npnct08.log 0.926 0.3547
## `prdline.my.fctriPad 1` 1.769 0.0768 .
## `prdline.my.fctriPad 2` 0.624 0.5328
## `prdline.my.fctriPad 3+` 1.450 0.1471
## prdline.my.fctriPadAir 2.101 0.0357 *
## prdline.my.fctriPadmini 1.092 0.2748
## `prdline.my.fctriPadmini 2+` 1.529 0.1262
## D.nstopwrds.log 1.920 0.0549 .
## D.npnct16.log 1.543 0.1227
## D.npnct24.log -0.853 0.3936
## D.npnct06.log -2.512 0.0120 *
## D.npnct28.log -0.002 0.9980
## D.nuppr.log 0.679 0.4974
## D.nchrs.log -0.761 0.4465
## D.nwrds.log -0.079 0.9373
## D.npnct12.log 0.807 0.4199
## carrier.fctrNone 0.309 0.7573
## carrier.fctrOther 0.009 0.9931
## carrier.fctrSprint 1.548 0.1217
## `carrier.fctrT-Mobile` -0.725 0.4683
## carrier.fctrUnknown -0.194 0.8460
## carrier.fctrVerizon 1.039 0.2990
## D.npnct09.log -0.009 0.9924
## D.ndgts.log 0.924 0.3554
## D.nwrds.unq.log -1.454 0.1459
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 1.453 0.1463
## cellular.fctr1 0.418 0.6756
## cellular.fctrUnknown NA NA
## D.npnct14.log -2.163 0.0305 *
## D.terms.n.post.stem 1.589 0.1121
## D.terms.n.post.stop -1.596 0.1106
## D.npnct05.log -1.467 0.1423
## `condition.fctrFor parts or not working` -0.077 0.9390
## `condition.fctrManufacturer refurbished` 0.842 0.3998
## condition.fctrNew -0.726 0.4678
## `condition.fctrNew other (see details)` -0.133 0.8942
## `condition.fctrSeller refurbished` -0.751 0.4526
## idseq.my -0.719 0.4724
## startprice.diff -8.811 <2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.269 0.0233 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.911 0.3624
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.151 0.2497
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.153 0.8782
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.184 0.8542
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.366 0.1720
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.565 0.5718
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.170 0.8653
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.143 0.8864
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.259 0.7953
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.108 0.9136
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.892 0.3722
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.102 0.2704
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.080 0.9364
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.631 0.1030
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.175 0.0296 *
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.822 0.4110
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.428 0.6685
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -1.318 0.1876
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.747 0.4548
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.016 0.9875
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 784.39 on 892 degrees of freedom
## AIC: 948.39
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7130008
## 3 0.2 0.7621671
## 4 0.3 0.7709163
## 5 0.4 0.7978610
## 6 0.5 0.8132875
## 7 0.6 0.8062575
## 8 0.7 0.7888041
## 9 0.8 0.6972222
## 10 0.9 0.5138211
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 456 68
## 2 Y 95 355
## Prediction
## Reference N Y
## N 456 68
## Y 95 355
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326489e-01 6.619222e-01 8.076954e-01 8.555717e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 4.994716e-84 4.170246e-02
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6994536
## 3 0.2 0.7197581
## 4 0.3 0.7449664
## 5 0.4 0.7614458
## 6 0.5 0.7715736
## 7 0.6 0.7710526
## 8 0.7 0.7622951
## 9 0.8 0.6786248
## 10 0.9 0.5061947
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glm.N sold.fctr.predict.All.X.glm.Y
## 1 N 401 74
## 2 Y 106 304
## Prediction
## Reference N Y
## N 401 74
## Y 106 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.966102e-01 5.888183e-01 7.685578e-01 8.226715e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.323328e-58 2.085476e-02
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.801 0.258
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.893698 0.5 0.8132875 0.767955
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8076954 0.8555717 0.5327262 0.8431528
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7715736 0.7966102
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7685578 0.8226715 0.5888183 948.3929
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01277838 0.0264692
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_glm 2 0 165.688 171.996 6.308
## 3 fit.models_1_bayesglm 3 0 171.996 NA NA
## [1] "fitting model: All.X.bayesglm"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5621 -0.6492 -0.1474 0.5758 2.8296
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 6.4732613 6.5967327
## biddable 3.1415025 0.2236448
## D.ratio.nstopwrds.nwrds -2.0016485 2.4122284
## D.npnct15.log 1.4920678 0.8898038
## D.npnct03.log 0.7287085 1.6791658
## D.terms.n.stem.stop.Ratio -5.3051998 5.2920393
## D.ratio.sum.TfIdf.nwrds -0.1788281 0.2804948
## .rnorm 0.0442853 0.0941655
## D.npnct01.log 0.3019465 0.5466944
## D.TfIdf.sum.stem.stop.Ratio -0.2632203 3.3867123
## storage.fctr16 -0.4268016 0.4513403
## storage.fctr32 -0.6090960 0.4796292
## storage.fctr64 0.0215054 0.4745300
## storage.fctrUnknown -0.7801934 0.6240624
## D.npnct11.log 0.1354597 0.3431836
## D.npnct10.log -7.8702366 7.4401293
## D.TfIdf.sum.post.stop 0.0622741 0.2937135
## D.npnct13.log 0.0796768 0.3410379
## D.TfIdf.sum.post.stem 0.0817057 0.3101478
## D.sum.TfIdf 0.0817057 0.3101478
## color.fctrGold 0.0738722 0.5380469
## `color.fctrSpace Gray` -0.4843851 0.3591170
## color.fctrUnknown -0.0557312 0.2473266
## color.fctrWhite -0.3312580 0.2674640
## D.npnct08.log 0.4948063 0.7028659
## `prdline.my.fctriPad 1` 0.7439913 0.4859660
## `prdline.my.fctriPad 2` 0.2259026 0.4944116
## `prdline.my.fctriPad 3+` 0.5746895 0.4674085
## prdline.my.fctriPadAir 0.8999477 0.4688732
## prdline.my.fctriPadmini 0.3800660 0.4563442
## `prdline.my.fctriPadmini 2+` 0.6052630 0.4998567
## D.nstopwrds.log 0.6193376 0.6513100
## D.npnct16.log 2.4321111 1.5861158
## D.npnct24.log -0.1571228 2.3237832
## D.npnct06.log -4.8219145 1.9065228
## D.npnct28.log -0.0917201 2.1756298
## D.nuppr.log 0.0282537 0.4728189
## D.nchrs.log -0.0773765 0.4760178
## D.nwrds.log -0.0694265 0.7684373
## D.npnct12.log 0.4095706 0.6865852
## carrier.fctrNone 0.0509338 1.1589591
## carrier.fctrOther 0.5232556 1.8491401
## carrier.fctrSprint 1.0981275 0.7406424
## `carrier.fctrT-Mobile` -0.6858277 0.8748829
## carrier.fctrUnknown -0.1980645 0.4785585
## carrier.fctrVerizon 0.4245266 0.4480254
## D.npnct09.log -1.8039793 5.2114152
## D.ndgts.log 0.6500518 0.4167512
## D.nwrds.unq.log -0.2693509 1.0356264
## D.terms.n.post.stem.log -0.2693509 1.0356264
## D.terms.n.post.stop.log -0.2650471 1.0315822
## cellular.fctr1 0.1280323 1.1501476
## cellular.fctrUnknown -0.1820448 1.1993917
## D.npnct14.log -2.3968587 1.0438074
## D.terms.n.post.stem -0.0861699 0.2053404
## D.terms.n.post.stop -0.1096726 0.2039429
## D.npnct05.log -2.3408280 1.4309601
## `condition.fctrFor parts or not working` 0.0178713 0.3598336
## `condition.fctrManufacturer refurbished` 0.5587583 0.6008105
## condition.fctrNew -0.2185260 0.3092958
## `condition.fctrNew other (see details)` -0.0413030 0.4603463
## `condition.fctrSeller refurbished` -0.2859324 0.4135467
## idseq.my -0.0001842 0.0002020
## startprice.diff -0.0112824 0.0012677
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.3773335 0.6569271
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.6830251 0.7189179
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.6950682 0.6281976
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.0494174 0.6273654
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.1582895 0.5942248
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.8263218 0.6677774
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.3121617 0.8178211
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.3238598 0.8390054
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.0215532 0.7847840
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.5628903 1.0369246
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.1492070 0.7940398
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.6366807 0.7663736
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.9188031 0.7898413
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.1325030 0.8295810
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.4353422 0.7804594
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.6373335 1.1479779
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.6223773 0.7899497
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.3661226 0.7762206
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.0000000 2.5000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -0.9053648 0.8033085
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.8143261 1.0989636
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.0000000 2.5000000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.0000000 2.5000000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.0000000 2.5000000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.0000000 2.5000000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.1439156 1.5833740
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.0000000 2.5000000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.0000000 2.5000000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.0000000 2.5000000
## z value Pr(>|z|)
## (Intercept) 0.981 0.3265
## biddable 14.047 <2e-16 ***
## D.ratio.nstopwrds.nwrds -0.830 0.4067
## D.npnct15.log 1.677 0.0936 .
## D.npnct03.log 0.434 0.6643
## D.terms.n.stem.stop.Ratio -1.002 0.3161
## D.ratio.sum.TfIdf.nwrds -0.638 0.5238
## .rnorm 0.470 0.6381
## D.npnct01.log 0.552 0.5807
## D.TfIdf.sum.stem.stop.Ratio -0.078 0.9380
## storage.fctr16 -0.946 0.3443
## storage.fctr32 -1.270 0.2041
## storage.fctr64 0.045 0.9639
## storage.fctrUnknown -1.250 0.2112
## D.npnct11.log 0.395 0.6931
## D.npnct10.log -1.058 0.2901
## D.TfIdf.sum.post.stop 0.212 0.8321
## D.npnct13.log 0.234 0.8153
## D.TfIdf.sum.post.stem 0.263 0.7922
## D.sum.TfIdf 0.263 0.7922
## color.fctrGold 0.137 0.8908
## `color.fctrSpace Gray` -1.349 0.1774
## color.fctrUnknown -0.225 0.8217
## color.fctrWhite -1.239 0.2155
## D.npnct08.log 0.704 0.4814
## `prdline.my.fctriPad 1` 1.531 0.1258
## `prdline.my.fctriPad 2` 0.457 0.6477
## `prdline.my.fctriPad 3+` 1.230 0.2189
## prdline.my.fctriPadAir 1.919 0.0549 .
## prdline.my.fctriPadmini 0.833 0.4049
## `prdline.my.fctriPadmini 2+` 1.211 0.2259
## D.nstopwrds.log 0.951 0.3416
## D.npnct16.log 1.533 0.1252
## D.npnct24.log -0.068 0.9461
## D.npnct06.log -2.529 0.0114 *
## D.npnct28.log -0.042 0.9664
## D.nuppr.log 0.060 0.9524
## D.nchrs.log -0.163 0.8709
## D.nwrds.log -0.090 0.9280
## D.npnct12.log 0.597 0.5508
## carrier.fctrNone 0.044 0.9649
## carrier.fctrOther 0.283 0.7772
## carrier.fctrSprint 1.483 0.1382
## `carrier.fctrT-Mobile` -0.784 0.4331
## carrier.fctrUnknown -0.414 0.6790
## carrier.fctrVerizon 0.948 0.3434
## D.npnct09.log -0.346 0.7292
## D.ndgts.log 1.560 0.1188
## D.nwrds.unq.log -0.260 0.7948
## D.terms.n.post.stem.log -0.260 0.7948
## D.terms.n.post.stop.log -0.257 0.7972
## cellular.fctr1 0.111 0.9114
## cellular.fctrUnknown -0.152 0.8794
## D.npnct14.log -2.296 0.0217 *
## D.terms.n.post.stem -0.420 0.6747
## D.terms.n.post.stop -0.538 0.5907
## D.npnct05.log -1.636 0.1019
## `condition.fctrFor parts or not working` 0.050 0.9604
## `condition.fctrManufacturer refurbished` 0.930 0.3524
## condition.fctrNew -0.707 0.4799
## `condition.fctrNew other (see details)` -0.090 0.9285
## `condition.fctrSeller refurbished` -0.691 0.4893
## idseq.my -0.912 0.3616
## startprice.diff -8.900 <2e-16 ***
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.097 0.0360 *
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.950 0.3421
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.106 0.2685
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.079 0.9372
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.266 0.7899
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.237 0.2159
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` -0.382 0.7027
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.386 0.6995
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.027 0.9781
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.543 0.5872
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.188 0.8509
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.831 0.4061
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.163 0.2447
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.160 0.8731
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.839 0.0659 .
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.297 0.0216 *
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.788 0.4308
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.472 0.6372
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.0000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -1.127 0.2597
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.741 0.4587
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.0000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.000 1.0000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.000 1.0000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.000 1.0000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.722 0.4700
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.000 1.0000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.000 1.0000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.000 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 794.78 on 875 degrees of freedom
## AIC: 992.78
##
## Number of Fisher Scoring iterations: 14
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7052209
## 3 0.2 0.7554745
## 4 0.3 0.7696909
## 5 0.4 0.7859425
## 6 0.5 0.8127128
## 7 0.6 0.8004837
## 8 0.7 0.7783505
## 9 0.8 0.6946779
## 10 0.9 0.4731544
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 451
## 2 Y 92
## sold.fctr.predict.All.X.bayesglm.Y
## 1 73
## 2 358
## Prediction
## Reference N Y
## N 451 73
## Y 92 358
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.305955e-01 6.582049e-01 8.055345e-01 8.536386e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 8.982639e-83 1.611249e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6958855
## 3 0.2 0.7225549
## 4 0.3 0.7452725
## 5 0.4 0.7572816
## 6 0.5 0.7708067
## 7 0.6 0.7669774
## 8 0.7 0.7527778
## 9 0.8 0.6858006
## 10 0.9 0.4688645
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.bayesglm.N
## 1 N 405
## 2 Y 109
## sold.fctr.predict.All.X.bayesglm.Y
## 1 70
## 2 301
## Prediction
## Reference N Y
## N 405 70
## Y 109 301
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.977401e-01 5.906219e-01 7.697388e-01 8.237428e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 3.897512e-59 4.507772e-03
## model_id model_method
## 1 All.X.bayesglm bayesglm
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 3.602 0.371
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8912129 0.5 0.8127128 0.7638683
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8055345 0.8536386 0.524377 0.846285
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7708067 0.7977401
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7697388 0.8237428 0.5906219 992.7758
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007372855 0.01470821
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_bayesglm 3 0 171.996 188.908 16.912
## 4 fit.models_1_glmnet 4 0 188.909 NA NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: glmnet
## Loaded glmnet 2.0-2
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 86 -none- numeric
## beta 8428 dgCMatrix S4
## df 86 -none- numeric
## dim 2 -none- numeric
## lambda 86 -none- numeric
## dev.ratio 86 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 98 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable startprice.diff
## -0.919515342 1.946620085 -0.004400296
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.276539e+01
## biddable
## 3.247199e+00
## D.ratio.nstopwrds.nwrds
## -9.083982e+00
## D.npnct15.log
## 1.407742e+00
## D.npnct03.log
## 9.912413e-01
## D.terms.n.stem.stop.Ratio
## -4.340034e+00
## D.ratio.sum.TfIdf.nwrds
## -2.183396e-01
## .rnorm
## 5.021253e-02
## D.npnct01.log
## 2.364494e-01
## D.TfIdf.sum.stem.stop.Ratio
## -5.113291e-01
## storage.fctr16
## -5.187503e-01
## storage.fctr32
## -7.161513e-01
## storage.fctr64
## -6.394010e-02
## storage.fctrUnknown
## -9.483757e-01
## D.npnct11.log
## 1.224066e-01
## D.npnct10.log
## -1.103133e+01
## D.TfIdf.sum.post.stop
## 4.139503e-02
## D.npnct13.log
## 3.052695e-02
## D.TfIdf.sum.post.stem
## 1.855788e-01
## D.sum.TfIdf
## 1.093133e-02
## color.fctrGold
## 4.367830e-02
## color.fctrSpace Gray
## -5.181102e-01
## color.fctrUnknown
## -6.875321e-02
## color.fctrWhite
## -3.460863e-01
## D.npnct08.log
## 5.029262e-01
## prdline.my.fctriPad 1
## 9.566672e-01
## prdline.my.fctriPad 2
## 3.352388e-01
## prdline.my.fctriPad 3+
## 7.665349e-01
## prdline.my.fctriPadAir
## 1.114513e+00
## prdline.my.fctriPadmini
## 5.437103e-01
## prdline.my.fctriPadmini 2+
## 8.284349e-01
## D.nstopwrds.log
## 2.295466e+00
## D.npnct16.log
## 2.776479e+00
## D.npnct24.log
## -2.040182e+00
## D.npnct06.log
## -5.580554e+00
## D.npnct28.log
## -1.600276e+00
## D.nchrs.log
## -4.892271e-01
## D.nwrds.log
## -3.992016e-03
## D.npnct12.log
## 5.291397e-01
## carrier.fctrOther
## 3.840250e+00
## carrier.fctrSprint
## 1.293093e+00
## carrier.fctrT-Mobile
## -7.690768e-01
## carrier.fctrUnknown
## -1.422833e-01
## carrier.fctrVerizon
## 4.995221e-01
## D.npnct09.log
## -1.302441e+00
## D.ndgts.log
## 5.801334e-01
## D.nwrds.unq.log
## -2.884429e+00
## cellular.fctr1
## 2.795507e-02
## cellular.fctrUnknown
## -2.033592e-01
## D.npnct14.log
## -2.668604e+00
## D.terms.n.post.stop
## -1.283063e-01
## D.npnct05.log
## -2.738838e+00
## condition.fctrFor parts or not working
## -3.326092e-02
## condition.fctrManufacturer refurbished
## 6.221347e-01
## condition.fctrNew
## -2.245634e-01
## condition.fctrNew other (see details)
## -4.730393e-02
## condition.fctrSeller refurbished
## -2.726138e-01
## idseq.my
## -1.612032e-04
## startprice.diff
## -1.173299e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.579022e+00
## prdline.my.fctriPad 1:.clusterid.fctr2
## 7.277815e-01
## prdline.my.fctriPad 2:.clusterid.fctr2
## 8.153899e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 7.426905e-03
## prdline.my.fctriPadAir:.clusterid.fctr2
## -1.764080e-01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 8.859192e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## -4.211580e-01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -1.560171e-01
## prdline.my.fctriPad 1:.clusterid.fctr3
## -2.946137e-02
## prdline.my.fctriPad 2:.clusterid.fctr3
## -7.809401e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -8.780368e-02
## prdline.my.fctriPadAir:.clusterid.fctr3
## -9.235025e-01
## prdline.my.fctriPadmini:.clusterid.fctr3
## 1.021235e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## -1.953260e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## -1.581805e+00
## prdline.my.fctriPad 2:.clusterid.fctr4
## 3.179580e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 7.783435e-01
## prdline.my.fctriPadmini:.clusterid.fctr4
## -4.596103e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5
## -1.218201e+00
## prdline.my.fctriPadmini:.clusterid.fctr5
## -1.151030e+00
## prdline.my.fctriPad 3+:.clusterid.fctr6
## -4.629991e+00
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.63202247
## 2 0.1 0.63784550
## 3 0.2 0.66818874
## 4 0.3 0.73653846
## 5 0.4 0.74725275
## 6 0.5 0.76244344
## 7 0.6 0.76666667
## 8 0.7 0.71369295
## 9 0.8 0.15132924
## 10 0.9 0.01324503
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 456
## 2 Y 128
## sold.fctr.predict.All.X.glmnet.Y
## 1 68
## 2 322
## Prediction
## Reference N Y
## N 456 68
## Y 128 322
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.987680e-01 5.913520e-01 7.721896e-01 8.235260e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.220245e-65 2.505699e-05
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.633204633
## 2 0.1 0.639498433
## 3 0.2 0.675126904
## 4 0.3 0.746582545
## 5 0.4 0.752157830
## 6 0.5 0.762626263
## 7 0.6 0.775132275
## 8 0.7 0.714285714
## 9 0.8 0.165548098
## 10 0.9 0.009708738
## 11 1.0 0.000000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 422
## 2 Y 117
## sold.fctr.predict.All.X.glmnet.Y
## 1 53
## 2 293
## Prediction
## Reference N Y
## N 422 53
## Y 117 293
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.079096e-01 6.095656e-01 7.803820e-01 8.333692e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.732450e-64 1.352502e-06
## model_id model_method
## 1 All.X.glmnet glmnet
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 5.826 1.167
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8592409 0.6 0.7666667 0.7864482
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7721896 0.823526 0.5692702 0.8631938
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7751323 0.8079096
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.780382 0.8333692 0.6095656
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004643467 0.01226196
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_glmnet 4 0 188.909 199.764 10.855
## 5 fit.models_1_rpart 5 0 199.765 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.02 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.51111111 0 1.0000000
## 2 0.08666667 1 0.4888889
## 3 0.02000000 2 0.4022222
##
## Variable importance
## biddable
## 54
## startprice.diff
## 23
## idseq.my
## 11
## condition.fctrFor parts or not working
## 3
## prdline.my.fctriPad 1
## 2
## prdline.my.fctriPad 2
## 2
## D.ratio.sum.TfIdf.nwrds
## 2
## condition.fctrNew
## 1
## condition.fctrNew other (see details)
## 1
## prdline.my.fctriPadmini 2+
## 1
## color.fctrGold
## 1
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 1
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff < 59.64413 to the right, improve= 48.01938, (0 missing)
## idseq.my < 905.5 to the right, improve= 38.91299, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 10.86739, (0 missing)
## D.nwrds.unq.log < 2.138333 to the right, improve= 9.59880, (0 missing)
## Surrogate splits:
## idseq.my < 869 to the right, agree=0.636, adj=0.211, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.563, adj=0.053, (0 split)
## prdline.my.fctriPad 1 < 0.5 to the left, agree=0.559, adj=0.044, (0 split)
## prdline.my.fctriPad 2 < 0.5 to the left, agree=0.553, adj=0.033, (0 split)
## D.ratio.sum.TfIdf.nwrds < 0.9315387 to the left, agree=0.551, adj=0.029, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.08666667
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (135 obs) right son=7 (315 obs)
## Primary splits:
## startprice.diff < 68.91842 to the right, improve=61.714290, (0 missing)
## idseq.my < 670.5 to the right, improve=15.110620, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.467222, (0 missing)
## carrier.fctrUnknown < 0.5 to the right, improve= 3.110995, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve= 2.722222, (0 missing)
## Surrogate splits:
## condition.fctrNew < 0.5 to the right, agree=0.713, adj=0.044, (0 split)
## prdline.my.fctriPadmini 2+ < 0.5 to the right, agree=0.709, adj=0.030, (0 split)
## condition.fctrNew other (see details) < 0.5 to the right, agree=0.709, adj=0.030, (0 split)
## color.fctrGold < 0.5 to the right, agree=0.707, adj=0.022, (0 split)
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 < 0.5 to the right, agree=0.707, adj=0.022, (0 split)
##
## Node number 6: 135 observations
## predicted class=N expected loss=0.3555556 P(node) =0.1386037
## class counts: 87 48
## probabilities: 0.644 0.356
##
## Node number 7: 315 observations
## predicted class=Y expected loss=0.07301587 P(node) =0.3234086
## class counts: 23 292
## probabilities: 0.073 0.927
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=68.91842 135 48 N (0.64444444 0.35555556) *
## 7) startprice.diff< 68.91842 315 23 Y (0.07301587 0.92698413) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7555556
## 5 0.4 0.7633987
## 6 0.5 0.7633987
## 7 0.6 0.7633987
## 8 0.7 0.7633987
## 9 0.8 0.7633987
## 10 0.9 0.7633987
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 501
## 2 Y 158
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 23
## 2 292
## Prediction
## Reference N Y
## N 501 23
## Y 158 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.141684e-01 6.180889e-01 7.882906e-01 8.381305e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.530340e-73 2.277382e-23
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7528231
## 5 0.4 0.7420290
## 6 0.5 0.7420290
## 7 0.6 0.7420290
## 8 0.7 0.7420290
## 9 0.8 0.7420290
## 10 0.9 0.7420290
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rpart.N
## 1 N 388
## 2 Y 110
## sold.fctr.predict.All.X.no.rnorm.rpart.Y
## 1 87
## 2 300
## Prediction
## Reference N Y
## N 388 87
## Y 110 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.774011e-01 5.506630e-01 7.485294e-01 8.044124e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.956102e-50 1.170130e-01
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.673 0.069
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8243427 0.9 0.7633987 0.8018645
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7882906 0.8381305 0.5940341 0.8129705
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7528231 0.7774011
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7485294 0.8044124 0.550663
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01388821 0.02957203
## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rpart 5 0 199.765 205.638 5.873
## 6 fit.models_1_rf 6 0 205.638 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 49 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 97 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 97 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8249313
## 3 0.2 0.9433962
## 4 0.3 0.9814613
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 1.0000000
## 8 0.7 0.9522701
## 9 0.8 0.8650694
## 10 0.9 0.7721692
## 11 1.0 0.1171548
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 524
## 2 Y NA
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 NA
## 2 450
## Prediction
## Reference N Y
## N 524 0
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962198e-01 1.000000e+00 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.919016e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.63320463
## 2 0.1 0.68221071
## 3 0.2 0.73034826
## 4 0.3 0.76049943
## 5 0.4 0.78378378
## 6 0.5 0.78543563
## 7 0.6 0.77329624
## 8 0.7 0.75644699
## 9 0.8 0.71171171
## 10 0.9 0.65280000
## 11 1.0 0.04295943## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.X.no.rnorm.rf.N
## 1 N 418
## 2 Y 108
## sold.fctr.predict.All.X.no.rnorm.rf.Y
## 1 57
## 2 302
## Prediction
## Reference N Y
## N 418 57
## Y 108 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.135593e-01 6.218781e-01 7.863067e-01 8.387053e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 6.832889e-67 9.921866e-05
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 16.547 5.082
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.6 1 0.8018392
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962198 1 0.597041 0.8637792
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7854356 0.8135593
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7863067 0.8387053 0.6218781
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01439889 0.02854761
## label step_major step_minor bgn end elapsed
## 6 fit.models_1_rf 6 0 205.638 225.897 20.259
## 7 fit.models_1_glm 7 0 225.897 NA NA
## [1] "fitting model: All.Interact.X.glm"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 62, 672, 951
## Warning: not plotting observations with leverage one:
## 62, 672, 951
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.02835 -0.61392 -0.07853 0.44023 2.74561
##
## Coefficients: (32 not defined because of singularities)
## Estimate
## (Intercept) -1.766e+03
## D.ratio.nstopwrds.nwrds -1.737e+01
## D.terms.n.stem.stop.Ratio 1.773e+03
## .rnorm 3.075e-02
## D.npnct01.log 6.111e-01
## storage.fctr16 -3.510e-01
## storage.fctr32 -5.069e-01
## storage.fctr64 8.672e-02
## storage.fctrUnknown -7.415e-01
## D.npnct11.log -3.206e-02
## D.npnct10.log -2.644e+01
## D.TfIdf.sum.post.stop -7.422e-01
## D.npnct13.log 1.445e-01
## D.TfIdf.sum.post.stem 8.595e-01
## D.sum.TfIdf NA
## color.fctrGold -3.116e-01
## `color.fctrSpace Gray` -6.135e-01
## color.fctrUnknown -2.955e-01
## color.fctrWhite -4.178e-01
## D.npnct08.log 6.326e-01
## `prdline.my.fctriPad 1` 2.995e+01
## `prdline.my.fctriPad 2` 2.279e+01
## `prdline.my.fctriPad 3+` 1.776e+01
## prdline.my.fctriPadAir 9.237e+00
## prdline.my.fctriPadmini 1.089e+01
## `prdline.my.fctriPadmini 2+` 1.613e+01
## D.nstopwrds.log 4.470e+00
## D.npnct16.log 2.071e+00
## D.npnct24.log -6.124e+00
## D.npnct06.log -5.102e+00
## D.npnct28.log -2.621e+00
## D.nuppr.log 1.575e+00
## D.nchrs.log -2.257e+00
## D.nwrds.log -5.270e-01
## D.npnct12.log 6.089e-01
## D.npnct09.log -9.740e+00
## D.ndgts.log 4.734e-01
## D.nwrds.unq.log -2.044e+03
## D.terms.n.post.stem.log NA
## D.terms.n.post.stop.log 2.040e+03
## D.npnct14.log -2.673e+00
## D.terms.n.post.stem 1.711e+01
## D.terms.n.post.stop -1.726e+01
## D.npnct05.log -3.244e+00
## `condition.fctrFor parts or not working` -2.454e-01
## `condition.fctrManufacturer refurbished` 3.344e-01
## condition.fctrNew -1.873e-01
## `condition.fctrNew other (see details)` 9.792e-02
## `condition.fctrSeller refurbished` -5.681e-01
## idseq.my 8.131e-04
## D.ratio.sum.TfIdf.nwrds 4.714e-01
## D.TfIdf.sum.stem.stop.Ratio 8.321e+00
## D.npnct15.log -2.104e+01
## D.npnct03.log 4.328e-01
## startprice.diff -6.002e-03
## biddable 3.981e+00
## cellular.fctr1 2.914e-03
## cellular.fctrUnknown -4.209e-01
## carrier.fctrNone NA
## carrier.fctrOther 1.526e+01
## carrier.fctrSprint 1.198e+00
## `carrier.fctrT-Mobile` -1.138e+00
## carrier.fctrUnknown 7.745e-02
## carrier.fctrVerizon 4.363e-01
## `prdline.my.fctriPad 1:idseq.my` -3.989e-04
## `prdline.my.fctriPad 2:idseq.my` -1.450e-03
## `prdline.my.fctriPad 3+:idseq.my` -6.034e-04
## `prdline.my.fctriPadAir:idseq.my` -1.877e-03
## `prdline.my.fctriPadmini:idseq.my` -9.396e-04
## `prdline.my.fctriPadmini 2+:idseq.my` -4.648e-04
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` -6.382e-01
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 1.926e+00
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` -3.913e-01
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` -1.427e+00
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` -1.336e+00
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` -2.559e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -2.852e+01
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -2.134e+01
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -1.630e+01
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -6.101e+00
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -9.374e+00
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -1.468e+01
## `prdline.my.fctriPad 1:D.npnct15.log` 4.099e+01
## `prdline.my.fctriPad 2:D.npnct15.log` 4.322e+01
## `prdline.my.fctriPad 3+:D.npnct15.log` 1.952e+01
## `prdline.my.fctriPadAir:D.npnct15.log` 4.387e+01
## `prdline.my.fctriPadmini:D.npnct15.log` 4.826e+01
## `prdline.my.fctriPadmini 2+:D.npnct15.log` NA
## `prdline.my.fctriPad 1:D.npnct03.log` 2.514e+00
## `prdline.my.fctriPad 2:D.npnct03.log` 1.242e-01
## `prdline.my.fctriPad 3+:D.npnct03.log` -1.944e+01
## `prdline.my.fctriPadAir:D.npnct03.log` 1.333e+00
## `prdline.my.fctriPadmini:D.npnct03.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct03.log` NA
## `startprice.diff:biddable` -1.412e-02
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.964e+00
## `prdline.my.fctriPad 1:.clusterid.fctr2` -1.222e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -9.100e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -2.576e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 1.064e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.536e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.497e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -9.273e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.216e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.963e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -6.334e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 1.888e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.470e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.296e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.172e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.760e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 4.479e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -8.706e-04
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -1.476e+00
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -7.159e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.627e+01
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## Std. Error
## (Intercept) 2.698e+03
## D.ratio.nstopwrds.nwrds 8.000e+00
## D.terms.n.stem.stop.Ratio 2.700e+03
## .rnorm 1.046e-01
## D.npnct01.log 7.102e-01
## storage.fctr16 5.347e-01
## storage.fctr32 5.707e-01
## storage.fctr64 5.477e-01
## storage.fctrUnknown 7.446e-01
## D.npnct11.log 4.130e-01
## D.npnct10.log 2.018e+03
## D.TfIdf.sum.post.stop 3.625e+00
## D.npnct13.log 4.491e-01
## D.TfIdf.sum.post.stem 3.784e+00
## D.sum.TfIdf NA
## color.fctrGold 5.799e-01
## `color.fctrSpace Gray` 4.077e-01
## color.fctrUnknown 2.863e-01
## color.fctrWhite 3.086e-01
## D.npnct08.log 7.984e-01
## `prdline.my.fctriPad 1` 1.495e+01
## `prdline.my.fctriPad 2` 1.811e+01
## `prdline.my.fctriPad 3+` 1.126e+01
## prdline.my.fctriPadAir 1.504e+01
## prdline.my.fctriPadmini 1.249e+01
## `prdline.my.fctriPadmini 2+` 1.451e+01
## D.nstopwrds.log 2.225e+00
## D.npnct16.log 1.835e+00
## D.npnct24.log 7.168e+00
## D.npnct06.log 2.316e+00
## D.npnct28.log 2.874e+03
## D.nuppr.log 4.751e+00
## D.nchrs.log 5.450e+00
## D.nwrds.log 3.472e+00
## D.npnct12.log 8.256e-01
## D.npnct09.log 1.342e+03
## D.ndgts.log 6.149e-01
## D.nwrds.unq.log 3.024e+03
## D.terms.n.post.stem.log NA
## D.terms.n.post.stop.log 3.024e+03
## D.npnct14.log 1.327e+00
## D.terms.n.post.stem 1.846e+01
## D.terms.n.post.stop 1.841e+01
## D.npnct05.log 1.887e+00
## `condition.fctrFor parts or not working` 4.449e-01
## `condition.fctrManufacturer refurbished` 6.460e-01
## condition.fctrNew 3.368e-01
## `condition.fctrNew other (see details)` 5.103e-01
## `condition.fctrSeller refurbished` 4.822e-01
## idseq.my 7.051e-04
## D.ratio.sum.TfIdf.nwrds 7.365e-01
## D.TfIdf.sum.stem.stop.Ratio 2.512e+01
## D.npnct15.log 5.708e+03
## D.npnct03.log 2.619e+00
## startprice.diff 1.712e-03
## biddable 3.024e-01
## cellular.fctr1 3.301e-01
## cellular.fctrUnknown 7.642e-01
## carrier.fctrNone NA
## carrier.fctrOther 2.599e+03
## carrier.fctrSprint 7.871e-01
## `carrier.fctrT-Mobile` 1.133e+00
## carrier.fctrUnknown 5.355e-01
## carrier.fctrVerizon 5.132e-01
## `prdline.my.fctriPad 1:idseq.my` 8.327e-04
## `prdline.my.fctriPad 2:idseq.my` 9.582e-04
## `prdline.my.fctriPad 3+:idseq.my` 7.981e-04
## `prdline.my.fctriPadAir:idseq.my` 8.234e-04
## `prdline.my.fctriPadmini:idseq.my` 8.062e-04
## `prdline.my.fctriPadmini 2+:idseq.my` 8.780e-04
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` 7.972e-01
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 1.442e+00
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` 7.174e-01
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` 8.504e-01
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` 1.166e+00
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` 1.830e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 1.494e+01
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 1.805e+01
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 1.125e+01
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 1.503e+01
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 1.248e+01
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 1.452e+01
## `prdline.my.fctriPad 1:D.npnct15.log` 6.230e+03
## `prdline.my.fctriPad 2:D.npnct15.log` 8.072e+03
## `prdline.my.fctriPad 3+:D.npnct15.log` 5.708e+03
## `prdline.my.fctriPadAir:D.npnct15.log` 8.072e+03
## `prdline.my.fctriPadmini:D.npnct15.log` 6.792e+03
## `prdline.my.fctriPadmini 2+:D.npnct15.log` NA
## `prdline.my.fctriPad 1:D.npnct03.log` 5.983e+00
## `prdline.my.fctriPad 2:D.npnct03.log` 2.885e+00
## `prdline.my.fctriPad 3+:D.npnct03.log` 3.917e+03
## `prdline.my.fctriPadAir:D.npnct03.log` 2.928e+00
## `prdline.my.fctriPadmini:D.npnct03.log` NA
## `prdline.my.fctriPadmini 2+:D.npnct03.log` NA
## `startprice.diff:biddable` 2.662e-03
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrSprint` NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrUnknown` NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrVerizon` NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 9.147e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.041e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.407e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 8.357e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 8.549e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.099e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.320e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.291e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.223e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.899e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 1.062e+00
## `prdline.my.fctriPadAir:.clusterid.fctr3` 1.174e+00
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.262e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.462e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.211e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.918e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.025e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.169e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 1.080e+00
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.615e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 1.585e+03
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## z value Pr(>|z|)
## (Intercept) -0.655 0.512726
## D.ratio.nstopwrds.nwrds -2.171 0.029904
## D.terms.n.stem.stop.Ratio 0.657 0.511389
## .rnorm 0.294 0.768804
## D.npnct01.log 0.860 0.389530
## storage.fctr16 -0.656 0.511595
## storage.fctr32 -0.888 0.374468
## storage.fctr64 0.158 0.874182
## storage.fctrUnknown -0.996 0.319309
## D.npnct11.log -0.078 0.938127
## D.npnct10.log -0.013 0.989546
## D.TfIdf.sum.post.stop -0.205 0.837780
## D.npnct13.log 0.322 0.747643
## D.TfIdf.sum.post.stem 0.227 0.820294
## D.sum.TfIdf NA NA
## color.fctrGold -0.537 0.591090
## `color.fctrSpace Gray` -1.505 0.132325
## color.fctrUnknown -1.032 0.302064
## color.fctrWhite -1.354 0.175815
## D.npnct08.log 0.792 0.428178
## `prdline.my.fctriPad 1` 2.004 0.045090
## `prdline.my.fctriPad 2` 1.258 0.208233
## `prdline.my.fctriPad 3+` 1.578 0.114634
## prdline.my.fctriPadAir 0.614 0.539090
## prdline.my.fctriPadmini 0.872 0.383120
## `prdline.my.fctriPadmini 2+` 1.112 0.266066
## D.nstopwrds.log 2.009 0.044569
## D.npnct16.log 1.129 0.258895
## D.npnct24.log -0.854 0.392895
## D.npnct06.log -2.203 0.027598
## D.npnct28.log -0.001 0.999272
## D.nuppr.log 0.332 0.740263
## D.nchrs.log -0.414 0.678760
## D.nwrds.log -0.152 0.879336
## D.npnct12.log 0.738 0.460774
## D.npnct09.log -0.007 0.994211
## D.ndgts.log 0.770 0.441319
## D.nwrds.unq.log -0.676 0.499075
## D.terms.n.post.stem.log NA NA
## D.terms.n.post.stop.log 0.675 0.499868
## D.npnct14.log -2.014 0.043998
## D.terms.n.post.stem 0.927 0.354160
## D.terms.n.post.stop -0.938 0.348365
## D.npnct05.log -1.719 0.085622
## `condition.fctrFor parts or not working` -0.552 0.581264
## `condition.fctrManufacturer refurbished` 0.518 0.604718
## condition.fctrNew -0.556 0.578103
## `condition.fctrNew other (see details)` 0.192 0.847830
## `condition.fctrSeller refurbished` -1.178 0.238755
## idseq.my 1.153 0.248872
## D.ratio.sum.TfIdf.nwrds 0.640 0.522079
## D.TfIdf.sum.stem.stop.Ratio 0.331 0.740433
## D.npnct15.log -0.004 0.997058
## D.npnct03.log 0.165 0.868749
## startprice.diff -3.505 0.000457
## biddable 13.166 < 2e-16
## cellular.fctr1 0.009 0.992957
## cellular.fctrUnknown -0.551 0.581806
## carrier.fctrNone NA NA
## carrier.fctrOther 0.006 0.995313
## carrier.fctrSprint 1.522 0.128013
## `carrier.fctrT-Mobile` -1.004 0.315230
## carrier.fctrUnknown 0.145 0.884989
## carrier.fctrVerizon 0.850 0.395308
## `prdline.my.fctriPad 1:idseq.my` -0.479 0.631875
## `prdline.my.fctriPad 2:idseq.my` -1.513 0.130227
## `prdline.my.fctriPad 3+:idseq.my` -0.756 0.449659
## `prdline.my.fctriPadAir:idseq.my` -2.280 0.022621
## `prdline.my.fctriPadmini:idseq.my` -1.166 0.243814
## `prdline.my.fctriPadmini 2+:idseq.my` -0.529 0.596488
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` -0.801 0.423368
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 1.336 0.181672
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` -0.545 0.585435
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` -1.678 0.093345
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` -1.146 0.251898
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` -1.398 0.161984
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -1.909 0.056296
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` -1.182 0.237164
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` -1.449 0.147284
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` -0.406 0.684800
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` -0.751 0.452731
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` -1.011 0.312059
## `prdline.my.fctriPad 1:D.npnct15.log` 0.007 0.994751
## `prdline.my.fctriPad 2:D.npnct15.log` 0.005 0.995728
## `prdline.my.fctriPad 3+:D.npnct15.log` 0.003 0.997272
## `prdline.my.fctriPadAir:D.npnct15.log` 0.005 0.995663
## `prdline.my.fctriPadmini:D.npnct15.log` 0.007 0.994331
## `prdline.my.fctriPadmini 2+:D.npnct15.log` NA NA
## `prdline.my.fctriPad 1:D.npnct03.log` 0.420 0.674298
## `prdline.my.fctriPad 2:D.npnct03.log` 0.043 0.965667
## `prdline.my.fctriPad 3+:D.npnct03.log` -0.005 0.996040
## `prdline.my.fctriPadAir:D.npnct03.log` 0.455 0.648794
## `prdline.my.fctriPadmini:D.npnct03.log` NA NA
## `prdline.my.fctriPadmini 2+:D.npnct03.log` NA NA
## `startprice.diff:biddable` -5.303 1.14e-07
## `cellular.fctr1:carrier.fctrNone` NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA
## `cellular.fctr1:carrier.fctrOther` NA NA
## `cellular.fctrUnknown:carrier.fctrOther` NA NA
## `cellular.fctr1:carrier.fctrSprint` NA NA
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` NA NA
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA
## `cellular.fctr1:carrier.fctrUnknown` NA NA
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA
## `cellular.fctr1:carrier.fctrVerizon` NA NA
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr2` 2.148 0.031750
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.117 0.906500
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.647 0.517754
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.308 0.757943
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.124 0.900966
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.398 0.162259
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.265 0.791022
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.718 0.472599
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.994 0.320019
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.033 0.301408
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.597 0.550841
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.161 0.872212
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.165 0.244000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.886 0.375655
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.794 0.072813
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.917 0.358935
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.437 0.662164
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.001 0.999406
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -1.366 0.171922
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.443 0.657530
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.010 0.991806
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA NA
##
## (Intercept)
## D.ratio.nstopwrds.nwrds *
## D.terms.n.stem.stop.Ratio
## .rnorm
## D.npnct01.log
## storage.fctr16
## storage.fctr32
## storage.fctr64
## storage.fctrUnknown
## D.npnct11.log
## D.npnct10.log
## D.TfIdf.sum.post.stop
## D.npnct13.log
## D.TfIdf.sum.post.stem
## D.sum.TfIdf
## color.fctrGold
## `color.fctrSpace Gray`
## color.fctrUnknown
## color.fctrWhite
## D.npnct08.log
## `prdline.my.fctriPad 1` *
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## D.nstopwrds.log *
## D.npnct16.log
## D.npnct24.log
## D.npnct06.log *
## D.npnct28.log
## D.nuppr.log
## D.nchrs.log
## D.nwrds.log
## D.npnct12.log
## D.npnct09.log
## D.ndgts.log
## D.nwrds.unq.log
## D.terms.n.post.stem.log
## D.terms.n.post.stop.log
## D.npnct14.log *
## D.terms.n.post.stem
## D.terms.n.post.stop
## D.npnct05.log .
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## idseq.my
## D.ratio.sum.TfIdf.nwrds
## D.TfIdf.sum.stem.stop.Ratio
## D.npnct15.log
## D.npnct03.log
## startprice.diff ***
## biddable ***
## cellular.fctr1
## cellular.fctrUnknown
## carrier.fctrNone
## carrier.fctrOther
## carrier.fctrSprint
## `carrier.fctrT-Mobile`
## carrier.fctrUnknown
## carrier.fctrVerizon
## `prdline.my.fctriPad 1:idseq.my`
## `prdline.my.fctriPad 2:idseq.my`
## `prdline.my.fctriPad 3+:idseq.my`
## `prdline.my.fctriPadAir:idseq.my` *
## `prdline.my.fctriPadmini:idseq.my`
## `prdline.my.fctriPadmini 2+:idseq.my`
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` .
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` .
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 1:D.npnct15.log`
## `prdline.my.fctriPad 2:D.npnct15.log`
## `prdline.my.fctriPad 3+:D.npnct15.log`
## `prdline.my.fctriPadAir:D.npnct15.log`
## `prdline.my.fctriPadmini:D.npnct15.log`
## `prdline.my.fctriPadmini 2+:D.npnct15.log`
## `prdline.my.fctriPad 1:D.npnct03.log`
## `prdline.my.fctriPad 2:D.npnct03.log`
## `prdline.my.fctriPad 3+:D.npnct03.log`
## `prdline.my.fctriPadAir:D.npnct03.log`
## `prdline.my.fctriPadmini:D.npnct03.log`
## `prdline.my.fctriPadmini 2+:D.npnct03.log`
## `startprice.diff:biddable` ***
## `cellular.fctr1:carrier.fctrNone`
## `cellular.fctrUnknown:carrier.fctrNone`
## `cellular.fctr1:carrier.fctrOther`
## `cellular.fctrUnknown:carrier.fctrOther`
## `cellular.fctr1:carrier.fctrSprint`
## `cellular.fctrUnknown:carrier.fctrSprint`
## `cellular.fctr1:carrier.fctrT-Mobile`
## `cellular.fctrUnknown:carrier.fctrT-Mobile`
## `cellular.fctr1:carrier.fctrUnknown`
## `cellular.fctrUnknown:carrier.fctrUnknown`
## `cellular.fctr1:carrier.fctrVerizon`
## `cellular.fctrUnknown:carrier.fctrVerizon`
## `prdline.my.fctrUnknown:.clusterid.fctr2` *
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` .
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctrUnknown:.clusterid.fctr6`
## `prdline.my.fctriPad 1:.clusterid.fctr6`
## `prdline.my.fctriPad 2:.clusterid.fctr6`
## `prdline.my.fctriPad 3+:.clusterid.fctr6`
## `prdline.my.fctriPadAir:.clusterid.fctr6`
## `prdline.my.fctriPadmini:.clusterid.fctr6`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 722.93 on 864 degrees of freedom
## AIC: 942.93
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7130154
## 3 0.2 0.7655236
## 4 0.3 0.8033299
## 5 0.4 0.8219485
## 6 0.5 0.8135991
## 7 0.6 0.8199513
## 8 0.7 0.7979670
## 9 0.8 0.7550201
## 10 0.9 0.6359584
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.glm.N
## 1 N 448
## 2 Y 83
## sold.fctr.predict.All.Interact.X.glm.Y
## 1 76
## 2 367
## Prediction
## Reference N Y
## N 448 76
## Y 83 367
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.367556e-01 6.712547e-01 8.120210e-01 8.594339e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.413692e-86 6.341948e-01
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6936937
## 3 0.2 0.7303253
## 4 0.3 0.7384259
## 5 0.4 0.7487685
## 6 0.5 0.7573813
## 7 0.6 0.7526316
## 8 0.7 0.7352538
## 9 0.8 0.7146974
## 10 0.9 0.6096774
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.glm.N
## 1 N 401
## 2 Y 115
## sold.fctr.predict.All.Interact.X.glm.Y
## 1 74
## 2 295
## Prediction
## Reference N Y
## N 401 74
## Y 115 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.864407e-01 5.676063e-01 7.579436e-01 8.130160e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.826387e-54 3.619242e-03
## model_id model_method
## 1 All.Interact.X.glm glm
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.972 0.471
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9076718 0.4 0.8219485 0.7494967
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.812021 0.8594339 0.4949703 0.8344904
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7573813 0.7864407
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7579436 0.813016 0.5676063 942.9274
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01251751 0.02857686
## label step_major step_minor bgn end elapsed
## 7 fit.models_1_glm 7 0 225.897 231.893 5.996
## 8 fit.models_1_bayesglm 8 0 231.894 NA NA
## [1] "fitting model: All.Interact.X.bayesglm"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8981 -0.6415 -0.1496 0.4737 2.6704
##
## Coefficients:
## Estimate
## (Intercept) 7.111e+00
## D.ratio.nstopwrds.nwrds -1.783e+00
## D.terms.n.stem.stop.Ratio -6.355e+00
## .rnorm 3.703e-02
## D.npnct01.log 5.371e-01
## storage.fctr16 -2.814e-01
## storage.fctr32 -4.732e-01
## storage.fctr64 1.544e-01
## storage.fctrUnknown -5.737e-01
## D.npnct11.log -2.736e-02
## D.npnct10.log -7.845e+00
## D.TfIdf.sum.post.stop 4.921e-02
## D.npnct13.log 1.136e-01
## D.TfIdf.sum.post.stem 4.375e-02
## D.sum.TfIdf 4.375e-02
## color.fctrGold -2.026e-01
## `color.fctrSpace Gray` -5.641e-01
## color.fctrUnknown -2.446e-01
## color.fctrWhite -4.131e-01
## D.npnct08.log 4.873e-01
## `prdline.my.fctriPad 1` 8.426e-01
## `prdline.my.fctriPad 2` 3.475e-01
## `prdline.my.fctriPad 3+` 3.730e-01
## prdline.my.fctriPadAir 6.439e-01
## prdline.my.fctriPadmini 2.667e-01
## `prdline.my.fctriPadmini 2+` 2.414e-01
## D.nstopwrds.log 7.109e-01
## D.npnct16.log 2.272e+00
## D.npnct24.log -4.275e-01
## D.npnct06.log -4.614e+00
## D.npnct28.log -5.560e-02
## D.nuppr.log -1.436e-02
## D.nchrs.log -3.958e-02
## D.nwrds.log 5.699e-02
## D.npnct12.log 3.281e-01
## D.npnct09.log -1.700e+00
## D.ndgts.log 6.857e-01
## D.nwrds.unq.log -1.906e-01
## D.terms.n.post.stem.log -1.906e-01
## D.terms.n.post.stop.log -1.874e-01
## D.npnct14.log -2.228e+00
## D.terms.n.post.stem -9.356e-02
## D.terms.n.post.stop -1.261e-01
## D.npnct05.log -2.825e+00
## `condition.fctrFor parts or not working` -4.457e-02
## `condition.fctrManufacturer refurbished` 3.741e-01
## condition.fctrNew -1.669e-01
## `condition.fctrNew other (see details)` 8.592e-02
## `condition.fctrSeller refurbished` -3.657e-01
## idseq.my 2.303e-04
## D.ratio.sum.TfIdf.nwrds 3.642e-01
## D.TfIdf.sum.stem.stop.Ratio -5.954e-01
## D.npnct15.log 3.793e+00
## D.npnct03.log -6.694e-01
## startprice.diff -5.580e-03
## biddable 3.728e+00
## cellular.fctr1 1.087e-01
## cellular.fctrUnknown -1.398e-01
## carrier.fctrNone 1.083e-02
## carrier.fctrOther 4.667e-01
## carrier.fctrSprint 4.891e-01
## `carrier.fctrT-Mobile` -4.768e-01
## carrier.fctrUnknown -1.074e-01
## carrier.fctrVerizon 1.681e-01
## `prdline.my.fctriPad 1:idseq.my` 1.399e-04
## `prdline.my.fctriPad 2:idseq.my` -8.042e-04
## `prdline.my.fctriPad 3+:idseq.my` -1.500e-04
## `prdline.my.fctriPadAir:idseq.my` -1.249e-03
## `prdline.my.fctriPadmini:idseq.my` -3.985e-04
## `prdline.my.fctriPadmini 2+:idseq.my` 7.419e-05
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` -7.461e-01
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 2.078e+00
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` -2.760e-01
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` -1.020e+00
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` -7.400e-01
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` -1.810e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -7.521e-02
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 3.870e-01
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 4.472e-01
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 1.626e+00
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 5.434e-01
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 4.710e-01
## `prdline.my.fctriPad 1:D.npnct15.log` 2.428e+00
## `prdline.my.fctriPad 2:D.npnct15.log` 1.354e-01
## `prdline.my.fctriPad 3+:D.npnct15.log` -4.314e+00
## `prdline.my.fctriPadAir:D.npnct15.log` 1.889e-01
## `prdline.my.fctriPadmini:D.npnct15.log` 1.275e+00
## `prdline.my.fctriPadmini 2+:D.npnct15.log` -1.006e+00
## `prdline.my.fctriPad 1:D.npnct03.log` 2.468e+00
## `prdline.my.fctriPad 2:D.npnct03.log` 9.611e-01
## `prdline.my.fctriPad 3+:D.npnct03.log` -3.062e-01
## `prdline.my.fctriPadAir:D.npnct03.log` 9.216e-01
## `prdline.my.fctriPadmini:D.npnct03.log` 7.660e-01
## `prdline.my.fctriPadmini 2+:D.npnct03.log` -1.006e+00
## `startprice.diff:biddable` -1.300e-02
## `cellular.fctr1:carrier.fctrNone` 0.000e+00
## `cellular.fctrUnknown:carrier.fctrNone` 0.000e+00
## `cellular.fctr1:carrier.fctrOther` 4.667e-01
## `cellular.fctrUnknown:carrier.fctrOther` 0.000e+00
## `cellular.fctr1:carrier.fctrSprint` 4.891e-01
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000e+00
## `cellular.fctr1:carrier.fctrT-Mobile` -4.768e-01
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000e+00
## `cellular.fctr1:carrier.fctrUnknown` 1.272e-02
## `cellular.fctrUnknown:carrier.fctrUnknown` -1.398e-01
## `cellular.fctr1:carrier.fctrVerizon` 1.681e-01
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.348e+00
## `prdline.my.fctriPad 1:.clusterid.fctr2` 6.371e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` -3.081e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -6.389e-02
## `prdline.my.fctriPadAir:.clusterid.fctr2` -1.179e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 9.660e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 3.715e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.112e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.297e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.301e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -3.820e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -1.871e-02
## `prdline.my.fctriPadmini:.clusterid.fctr3` 9.504e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 7.684e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000e+00
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.553e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.595e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 5.120e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000e+00
## `prdline.my.fctriPadmini:.clusterid.fctr4` -2.224e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000e+00
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000e+00
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000e+00
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.000e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -8.268e-01
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000e+00
## `prdline.my.fctriPadmini:.clusterid.fctr5` -9.056e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000e+00
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.000e+00
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.000e+00
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.000e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.331e+00
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.000e+00
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.000e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.000e+00
## Std. Error
## (Intercept) 6.841e+00
## D.ratio.nstopwrds.nwrds 2.452e+00
## D.terms.n.stem.stop.Ratio 5.468e+00
## .rnorm 9.983e-02
## D.npnct01.log 5.613e-01
## storage.fctr16 4.578e-01
## storage.fctr32 4.905e-01
## storage.fctr64 4.772e-01
## storage.fctrUnknown 6.454e-01
## D.npnct11.log 3.566e-01
## D.npnct10.log 7.512e+00
## D.TfIdf.sum.post.stop 2.935e-01
## D.npnct13.log 3.617e-01
## D.TfIdf.sum.post.stem 3.084e-01
## D.sum.TfIdf 3.084e-01
## color.fctrGold 5.303e-01
## `color.fctrSpace Gray` 3.776e-01
## color.fctrUnknown 2.633e-01
## color.fctrWhite 2.853e-01
## D.npnct08.log 7.186e-01
## `prdline.my.fctriPad 1` 1.784e+00
## `prdline.my.fctriPad 2` 1.792e+00
## `prdline.my.fctriPad 3+` 1.632e+00
## prdline.my.fctriPadAir 1.722e+00
## prdline.my.fctriPadmini 1.684e+00
## `prdline.my.fctriPadmini 2+` 1.803e+00
## D.nstopwrds.log 6.657e-01
## D.npnct16.log 1.593e+00
## D.npnct24.log 2.490e+00
## D.npnct06.log 1.936e+00
## D.npnct28.log 2.186e+00
## D.nuppr.log 4.879e-01
## D.nchrs.log 4.799e-01
## D.nwrds.log 7.819e-01
## D.npnct12.log 7.356e-01
## D.npnct09.log 4.975e+00
## D.ndgts.log 4.413e-01
## D.nwrds.unq.log 1.034e+00
## D.terms.n.post.stem.log 1.034e+00
## D.terms.n.post.stop.log 1.030e+00
## D.npnct14.log 1.134e+00
## D.terms.n.post.stem 2.089e-01
## D.terms.n.post.stop 2.082e-01
## D.npnct05.log 1.492e+00
## `condition.fctrFor parts or not working` 4.034e-01
## `condition.fctrManufacturer refurbished` 6.009e-01
## condition.fctrNew 3.216e-01
## `condition.fctrNew other (see details)` 4.592e-01
## `condition.fctrSeller refurbished` 4.299e-01
## idseq.my 5.160e-04
## D.ratio.sum.TfIdf.nwrds 4.764e-01
## D.TfIdf.sum.stem.stop.Ratio 3.650e+00
## D.npnct15.log 3.401e+00
## D.npnct03.log 3.212e+00
## startprice.diff 1.583e-03
## biddable 2.737e-01
## cellular.fctr1 1.314e+00
## cellular.fctrUnknown 1.752e+00
## carrier.fctrNone 1.314e+00
## carrier.fctrOther 2.032e+00
## carrier.fctrSprint 1.530e+00
## `carrier.fctrT-Mobile` 1.577e+00
## carrier.fctrUnknown 1.327e+00
## carrier.fctrVerizon 1.472e+00
## `prdline.my.fctriPad 1:idseq.my` 6.577e-04
## `prdline.my.fctriPad 2:idseq.my` 7.711e-04
## `prdline.my.fctriPad 3+:idseq.my` 6.222e-04
## `prdline.my.fctriPadAir:idseq.my` 6.377e-04
## `prdline.my.fctriPadmini:idseq.my` 6.322e-04
## `prdline.my.fctriPadmini 2+:idseq.my` 6.986e-04
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` 5.151e-01
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 1.259e+00
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` 6.444e-01
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` 7.104e-01
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` 1.005e+00
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` 1.392e+00
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` 1.844e+00
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 1.858e+00
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 1.677e+00
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 1.765e+00
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 1.741e+00
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 1.897e+00
## `prdline.my.fctriPad 1:D.npnct15.log` 1.023e+01
## `prdline.my.fctriPad 2:D.npnct15.log` 3.345e+00
## `prdline.my.fctriPad 3+:D.npnct15.log` 3.760e+00
## `prdline.my.fctriPadAir:D.npnct15.log` 3.267e+00
## `prdline.my.fctriPadmini:D.npnct15.log` 2.978e+00
## `prdline.my.fctriPadmini 2+:D.npnct15.log` 3.152e+00
## `prdline.my.fctriPad 1:D.npnct03.log` 5.026e+00
## `prdline.my.fctriPad 2:D.npnct03.log` 3.684e+00
## `prdline.my.fctriPad 3+:D.npnct03.log` 3.129e+00
## `prdline.my.fctriPadAir:D.npnct03.log` 2.710e+00
## `prdline.my.fctriPadmini:D.npnct03.log` 3.317e+00
## `prdline.my.fctriPadmini 2+:D.npnct03.log` 3.152e+00
## `startprice.diff:biddable` 2.472e-03
## `cellular.fctr1:carrier.fctrNone` 2.500e+00
## `cellular.fctrUnknown:carrier.fctrNone` 2.500e+00
## `cellular.fctr1:carrier.fctrOther` 2.032e+00
## `cellular.fctrUnknown:carrier.fctrOther` 2.500e+00
## `cellular.fctr1:carrier.fctrSprint` 1.530e+00
## `cellular.fctrUnknown:carrier.fctrSprint` 2.500e+00
## `cellular.fctr1:carrier.fctrT-Mobile` 1.577e+00
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 2.500e+00
## `cellular.fctr1:carrier.fctrUnknown` 1.337e+00
## `cellular.fctrUnknown:carrier.fctrUnknown` 1.752e+00
## `cellular.fctr1:carrier.fctrVerizon` 1.472e+00
## `cellular.fctrUnknown:carrier.fctrVerizon` 2.500e+00
## `prdline.my.fctrUnknown:.clusterid.fctr2` 7.145e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 7.515e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 8.253e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 6.553e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 6.361e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 7.779e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 9.452e-01
## `prdline.my.fctrUnknown:.clusterid.fctr3` 1.015e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` 8.201e-01
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.150e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 8.375e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 9.088e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 8.744e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.090e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr4` 8.896e-01
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.265e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 8.071e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` 2.500e+00
## `prdline.my.fctriPadmini:.clusterid.fctr4` 8.756e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 2.500e+00
## `prdline.my.fctrUnknown:.clusterid.fctr5` 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr5` 2.500e+00
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.500e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 8.454e-01
## `prdline.my.fctriPadAir:.clusterid.fctr5` 2.500e+00
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.135e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 2.500e+00
## `prdline.my.fctrUnknown:.clusterid.fctr6` 2.500e+00
## `prdline.my.fctriPad 1:.clusterid.fctr6` 2.500e+00
## `prdline.my.fctriPad 2:.clusterid.fctr6` 2.500e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 1.561e+00
## `prdline.my.fctriPadAir:.clusterid.fctr6` 2.500e+00
## `prdline.my.fctriPadmini:.clusterid.fctr6` 2.500e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 2.500e+00
## z value Pr(>|z|)
## (Intercept) 1.039 0.298582
## D.ratio.nstopwrds.nwrds -0.727 0.467194
## D.terms.n.stem.stop.Ratio -1.162 0.245150
## .rnorm 0.371 0.710729
## D.npnct01.log 0.957 0.338657
## storage.fctr16 -0.615 0.538789
## storage.fctr32 -0.965 0.334764
## storage.fctr64 0.324 0.746300
## storage.fctrUnknown -0.889 0.374026
## D.npnct11.log -0.077 0.938838
## D.npnct10.log -1.044 0.296318
## D.TfIdf.sum.post.stop 0.168 0.866874
## D.npnct13.log 0.314 0.753360
## D.TfIdf.sum.post.stem 0.142 0.887197
## D.sum.TfIdf 0.142 0.887197
## color.fctrGold -0.382 0.702403
## `color.fctrSpace Gray` -1.494 0.135153
## color.fctrUnknown -0.929 0.353072
## color.fctrWhite -1.448 0.147554
## D.npnct08.log 0.678 0.497695
## `prdline.my.fctriPad 1` 0.472 0.636739
## `prdline.my.fctriPad 2` 0.194 0.846236
## `prdline.my.fctriPad 3+` 0.229 0.819191
## prdline.my.fctriPadAir 0.374 0.708515
## prdline.my.fctriPadmini 0.158 0.874189
## `prdline.my.fctriPadmini 2+` 0.134 0.893462
## D.nstopwrds.log 1.068 0.285611
## D.npnct16.log 1.426 0.153746
## D.npnct24.log -0.172 0.863652
## D.npnct06.log -2.383 0.017194
## D.npnct28.log -0.025 0.979705
## D.nuppr.log -0.029 0.976514
## D.nchrs.log -0.082 0.934264
## D.nwrds.log 0.073 0.941902
## D.npnct12.log 0.446 0.655618
## D.npnct09.log -0.342 0.732553
## D.ndgts.log 1.554 0.120255
## D.nwrds.unq.log -0.184 0.853728
## D.terms.n.post.stem.log -0.184 0.853728
## D.terms.n.post.stop.log -0.182 0.855570
## D.npnct14.log -1.964 0.049516
## D.terms.n.post.stem -0.448 0.654296
## D.terms.n.post.stop -0.606 0.544723
## D.npnct05.log -1.894 0.058222
## `condition.fctrFor parts or not working` -0.110 0.912030
## `condition.fctrManufacturer refurbished` 0.623 0.533537
## condition.fctrNew -0.519 0.603773
## `condition.fctrNew other (see details)` 0.187 0.851577
## `condition.fctrSeller refurbished` -0.851 0.394874
## idseq.my 0.446 0.655375
## D.ratio.sum.TfIdf.nwrds 0.765 0.444561
## D.TfIdf.sum.stem.stop.Ratio -0.163 0.870447
## D.npnct15.log 1.115 0.264654
## D.npnct03.log -0.208 0.834919
## startprice.diff -3.524 0.000425
## biddable 13.623 < 2e-16
## cellular.fctr1 0.083 0.934058
## cellular.fctrUnknown -0.080 0.936415
## carrier.fctrNone 0.008 0.993421
## carrier.fctrOther 0.230 0.818299
## carrier.fctrSprint 0.320 0.749292
## `carrier.fctrT-Mobile` -0.302 0.762460
## carrier.fctrUnknown -0.081 0.935519
## carrier.fctrVerizon 0.114 0.909118
## `prdline.my.fctriPad 1:idseq.my` 0.213 0.831592
## `prdline.my.fctriPad 2:idseq.my` -1.043 0.296941
## `prdline.my.fctriPad 3+:idseq.my` -0.241 0.809532
## `prdline.my.fctriPadAir:idseq.my` -1.959 0.050113
## `prdline.my.fctriPadmini:idseq.my` -0.630 0.528484
## `prdline.my.fctriPadmini 2+:idseq.my` 0.106 0.915426
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds` -1.448 0.147487
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` 1.650 0.098990
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds` -0.428 0.668447
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds` -1.436 0.151009
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds` -0.736 0.461625
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds` -1.300 0.193666
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio` -0.041 0.967475
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio` 0.208 0.835035
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio` 0.267 0.789665
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio` 0.921 0.356960
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio` 0.312 0.755022
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio` 0.248 0.803961
## `prdline.my.fctriPad 1:D.npnct15.log` 0.237 0.812410
## `prdline.my.fctriPad 2:D.npnct15.log` 0.040 0.967698
## `prdline.my.fctriPad 3+:D.npnct15.log` -1.147 0.251293
## `prdline.my.fctriPadAir:D.npnct15.log` 0.058 0.953896
## `prdline.my.fctriPadmini:D.npnct15.log` 0.428 0.668682
## `prdline.my.fctriPadmini 2+:D.npnct15.log` -0.319 0.749502
## `prdline.my.fctriPad 1:D.npnct03.log` 0.491 0.623381
## `prdline.my.fctriPad 2:D.npnct03.log` 0.261 0.794181
## `prdline.my.fctriPad 3+:D.npnct03.log` -0.098 0.922041
## `prdline.my.fctriPadAir:D.npnct03.log` 0.340 0.733743
## `prdline.my.fctriPadmini:D.npnct03.log` 0.231 0.817345
## `prdline.my.fctriPadmini 2+:D.npnct03.log` -0.319 0.749502
## `startprice.diff:biddable` -5.256 1.47e-07
## `cellular.fctr1:carrier.fctrNone` 0.000 1.000000
## `cellular.fctrUnknown:carrier.fctrNone` 0.000 1.000000
## `cellular.fctr1:carrier.fctrOther` 0.230 0.818299
## `cellular.fctrUnknown:carrier.fctrOther` 0.000 1.000000
## `cellular.fctr1:carrier.fctrSprint` 0.320 0.749292
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000 1.000000
## `cellular.fctr1:carrier.fctrT-Mobile` -0.302 0.762460
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000 1.000000
## `cellular.fctr1:carrier.fctrUnknown` 0.010 0.992408
## `cellular.fctrUnknown:carrier.fctrUnknown` -0.080 0.936415
## `cellular.fctr1:carrier.fctrVerizon` 0.114 0.909118
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.887 0.059227
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.848 0.396586
## `prdline.my.fctriPad 2:.clusterid.fctr2` -0.373 0.708915
## `prdline.my.fctriPad 3+:.clusterid.fctr2` -0.097 0.922339
## `prdline.my.fctriPadAir:.clusterid.fctr2` -0.185 0.852927
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.242 0.214323
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.393 0.694322
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.096 0.273207
## `prdline.my.fctriPad 1:.clusterid.fctr3` -0.158 0.874335
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.131 0.257955
## `prdline.my.fctriPad 3+:.clusterid.fctr3` -0.456 0.648277
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.021 0.983574
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.087 0.277086
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.705 0.480941
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -1.745 0.080919
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.261 0.207307
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.634 0.525848
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.254 0.799519
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` -0.978 0.328097
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.798 0.424915
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000 1.000000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.000 1.000000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.000 1.000000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.000 1.000000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.853 0.393895
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.000 1.000000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.000 1.000000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.000 1.000000
##
## (Intercept)
## D.ratio.nstopwrds.nwrds
## D.terms.n.stem.stop.Ratio
## .rnorm
## D.npnct01.log
## storage.fctr16
## storage.fctr32
## storage.fctr64
## storage.fctrUnknown
## D.npnct11.log
## D.npnct10.log
## D.TfIdf.sum.post.stop
## D.npnct13.log
## D.TfIdf.sum.post.stem
## D.sum.TfIdf
## color.fctrGold
## `color.fctrSpace Gray`
## color.fctrUnknown
## color.fctrWhite
## D.npnct08.log
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+`
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## D.nstopwrds.log
## D.npnct16.log
## D.npnct24.log
## D.npnct06.log *
## D.npnct28.log
## D.nuppr.log
## D.nchrs.log
## D.nwrds.log
## D.npnct12.log
## D.npnct09.log
## D.ndgts.log
## D.nwrds.unq.log
## D.terms.n.post.stem.log
## D.terms.n.post.stop.log
## D.npnct14.log *
## D.terms.n.post.stem
## D.terms.n.post.stop
## D.npnct05.log .
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## idseq.my
## D.ratio.sum.TfIdf.nwrds
## D.TfIdf.sum.stem.stop.Ratio
## D.npnct15.log
## D.npnct03.log
## startprice.diff ***
## biddable ***
## cellular.fctr1
## cellular.fctrUnknown
## carrier.fctrNone
## carrier.fctrOther
## carrier.fctrSprint
## `carrier.fctrT-Mobile`
## carrier.fctrUnknown
## carrier.fctrVerizon
## `prdline.my.fctriPad 1:idseq.my`
## `prdline.my.fctriPad 2:idseq.my`
## `prdline.my.fctriPad 3+:idseq.my`
## `prdline.my.fctriPadAir:idseq.my` .
## `prdline.my.fctriPadmini:idseq.my`
## `prdline.my.fctriPadmini 2+:idseq.my`
## `prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds` .
## `prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds`
## `prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio`
## `prdline.my.fctriPad 1:D.npnct15.log`
## `prdline.my.fctriPad 2:D.npnct15.log`
## `prdline.my.fctriPad 3+:D.npnct15.log`
## `prdline.my.fctriPadAir:D.npnct15.log`
## `prdline.my.fctriPadmini:D.npnct15.log`
## `prdline.my.fctriPadmini 2+:D.npnct15.log`
## `prdline.my.fctriPad 1:D.npnct03.log`
## `prdline.my.fctriPad 2:D.npnct03.log`
## `prdline.my.fctriPad 3+:D.npnct03.log`
## `prdline.my.fctriPadAir:D.npnct03.log`
## `prdline.my.fctriPadmini:D.npnct03.log`
## `prdline.my.fctriPadmini 2+:D.npnct03.log`
## `startprice.diff:biddable` ***
## `cellular.fctr1:carrier.fctrNone`
## `cellular.fctrUnknown:carrier.fctrNone`
## `cellular.fctr1:carrier.fctrOther`
## `cellular.fctrUnknown:carrier.fctrOther`
## `cellular.fctr1:carrier.fctrSprint`
## `cellular.fctrUnknown:carrier.fctrSprint`
## `cellular.fctr1:carrier.fctrT-Mobile`
## `cellular.fctrUnknown:carrier.fctrT-Mobile`
## `cellular.fctr1:carrier.fctrUnknown`
## `cellular.fctrUnknown:carrier.fctrUnknown`
## `cellular.fctr1:carrier.fctrVerizon`
## `cellular.fctrUnknown:carrier.fctrVerizon`
## `prdline.my.fctrUnknown:.clusterid.fctr2` .
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` .
## `prdline.my.fctriPad 2:.clusterid.fctr4`
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctrUnknown:.clusterid.fctr6`
## `prdline.my.fctriPad 1:.clusterid.fctr6`
## `prdline.my.fctriPad 2:.clusterid.fctr6`
## `prdline.my.fctriPad 3+:.clusterid.fctr6`
## `prdline.my.fctriPadAir:.clusterid.fctr6`
## `prdline.my.fctriPadmini:.clusterid.fctr6`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.6 on 973 degrees of freedom
## Residual deviance: 739.2 on 832 degrees of freedom
## AIC: 1023.2
##
## Number of Fisher Scoring iterations: 15
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6988906
## 3 0.2 0.7628676
## 4 0.3 0.7971014
## 5 0.4 0.8139013
## 6 0.5 0.8116959
## 7 0.6 0.8186275
## 8 0.7 0.8025478
## 9 0.8 0.7449393
## 10 0.9 0.6057839
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.bayesglm.N
## 1 N 492
## 2 Y 116
## sold.fctr.predict.All.Interact.X.bayesglm.Y
## 1 32
## 2 334
## Prediction
## Reference N Y
## N 492 32
## Y 116 334
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.480493e-01 6.902515e-01 8.239439e-01 8.700277e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.437206e-94 8.943352e-12
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6843034
## 3 0.2 0.7173690
## 4 0.3 0.7456446
## 5 0.4 0.7568238
## 6 0.5 0.7558442
## 7 0.6 0.7573333
## 8 0.7 0.7413555
## 9 0.8 0.7048458
## 10 0.9 0.5963756
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.bayesglm.N
## 1 N 419
## 2 Y 126
## sold.fctr.predict.All.Interact.X.bayesglm.Y
## 1 56
## 2 284
## Prediction
## Reference N Y
## N 419 56
## Y 126 284
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.943503e-01 5.815820e-01 7.661969e-01 8.205280e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.494158e-57 3.143727e-07
## model_id model_method
## 1 All.Interact.X.bayesglm bayesglm
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.669 0.644
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9015225 0.6 0.8186275 0.78132
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8239439 0.8700277 0.5584387 0.8364519
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7573333 0.7943503
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7661969 0.820528 0.581582 1023.197
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.004951066 0.01217108
## label step_major step_minor bgn end elapsed
## 8 fit.models_1_bayesglm 8 0 231.894 237.855 5.962
## 9 fit.models_1_glmnet 9 0 237.856 NA NA
## [1] "fitting model: All.Interact.X.glmnet"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 14100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 141 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -1.698226e-01
## D.terms.n.stem.stop.Ratio
## -3.659026e-01
## .rnorm
## 1.477174e-03
## D.npnct01.log
## 4.502176e-02
## storage.fctr32
## -2.449667e-02
## storage.fctr64
## 1.708322e-01
## storage.fctrUnknown
## -4.900348e-02
## D.npnct10.log
## -1.287469e+00
## D.TfIdf.sum.post.stop
## 2.469677e-03
## D.TfIdf.sum.post.stem
## 1.070756e-03
## D.sum.TfIdf
## 7.806662e-04
## color.fctrSpace Gray
## -3.439573e-02
## color.fctrWhite
## -1.063857e-01
## prdline.my.fctriPad 1
## 1.099817e-01
## prdline.my.fctriPadAir
## 2.724451e-02
## prdline.my.fctriPadmini 2+
## -7.900776e-03
## D.npnct06.log
## -4.962916e-01
## D.npnct09.log
## -2.047825e-01
## D.npnct14.log
## -7.695653e-01
## D.terms.n.post.stem
## -1.480411e-02
## D.terms.n.post.stop
## -1.373785e-02
## D.npnct05.log
## -1.096370e+00
## condition.fctrFor parts or not working
## 1.846066e-01
## condition.fctrNew
## -2.404425e-01
## condition.fctrSeller refurbished
## -2.866100e-02
## idseq.my
## -2.460823e-04
## D.ratio.sum.TfIdf.nwrds
## 9.474587e-03
## D.npnct15.log
## 1.882427e-01
## D.npnct03.log
## 1.588289e-01
## startprice.diff
## -3.468219e-03
## biddable
## 2.068567e+00
## cellular.fctrUnknown
## -1.157871e-01
## carrier.fctrOther
## 3.368476e-01
## carrier.fctrSprint
## 1.726424e-01
## carrier.fctrT-Mobile
## -1.152146e-01
## carrier.fctrUnknown
## -2.086499e-01
## carrier.fctrVerizon
## 5.088062e-02
## prdline.my.fctriPad 1:idseq.my
## 8.967734e-05
## prdline.my.fctriPad 2:idseq.my
## -8.974868e-05
## prdline.my.fctriPadAir:idseq.my
## -5.115439e-05
## prdline.my.fctriPadmini:idseq.my
## -4.324019e-06
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds
## -2.651698e-03
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds
## 8.741729e-01
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds
## 4.650224e-02
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds
## -1.009192e-03
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds
## -1.673170e-01
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## 6.246440e-02
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 4.218641e-02
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## -2.870275e-03
## prdline.my.fctriPad 1:D.npnct15.log
## 1.634943e+00
## prdline.my.fctriPad 2:D.npnct15.log
## 8.948154e-01
## prdline.my.fctriPad 3+:D.npnct15.log
## -2.201596e-01
## prdline.my.fctriPadAir:D.npnct15.log
## 1.098672e+00
## prdline.my.fctriPadmini:D.npnct15.log
## 2.521239e+00
## prdline.my.fctriPadmini 2+:D.npnct15.log
## -5.135175e-02
## prdline.my.fctriPad 1:D.npnct03.log
## 1.611671e-01
## prdline.my.fctriPad 3+:D.npnct03.log
## -6.311387e-02
## prdline.my.fctriPadmini:D.npnct03.log
## 3.290473e-01
## prdline.my.fctriPadmini 2+:D.npnct03.log
## -4.915287e-02
## startprice.diff:biddable
## -4.892813e-03
## cellular.fctr1:carrier.fctrOther
## 3.349700e-01
## cellular.fctr1:carrier.fctrSprint
## 1.723496e-01
## cellular.fctr1:carrier.fctrT-Mobile
## -1.145955e-01
## cellular.fctrUnknown:carrier.fctrUnknown
## -1.156738e-01
## cellular.fctr1:carrier.fctrVerizon
## 5.060152e-02
## prdline.my.fctrUnknown:.clusterid.fctr2
## 4.866790e-01
## prdline.my.fctriPad 1:.clusterid.fctr2
## 5.078194e-01
## prdline.my.fctriPadmini:.clusterid.fctr2
## 3.541130e-01
## prdline.my.fctrUnknown:.clusterid.fctr3
## -4.110006e-01
## prdline.my.fctriPad 2:.clusterid.fctr3
## -5.529884e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -3.477201e-03
## prdline.my.fctriPadAir:.clusterid.fctr3
## -7.637534e-02
## prdline.my.fctriPadmini:.clusterid.fctr3
## 2.714153e-01
## prdline.my.fctriPad 1:.clusterid.fctr4
## -7.104578e-01
## prdline.my.fctriPad 2:.clusterid.fctr4
## 1.240770e+00
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 7.759840e-02
## prdline.my.fctriPadmini:.clusterid.fctr4
## -9.085333e-02
## prdline.my.fctriPadmini:.clusterid.fctr5
## -2.503130e-01
## prdline.my.fctriPad 3+:.clusterid.fctr6
## -8.572935e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 9.4092544214
## D.ratio.nstopwrds.nwrds
## -4.0623637935
## D.terms.n.stem.stop.Ratio
## -6.9855363620
## .rnorm
## 0.0369428979
## D.npnct01.log
## 0.6165217699
## storage.fctr16
## -0.3542440642
## storage.fctr32
## -0.5531000946
## storage.fctr64
## 0.0792727873
## storage.fctrUnknown
## -0.7057754038
## D.npnct11.log
## -0.0557062969
## D.npnct10.log
## -9.9557016772
## D.TfIdf.sum.post.stop
## 0.0567305353
## D.npnct13.log
## 0.1090122594
## D.TfIdf.sum.post.stem
## 0.0321755950
## D.sum.TfIdf
## 0.0347095394
## color.fctrGold
## -0.2853923526
## color.fctrSpace Gray
## -0.6085422591
## color.fctrUnknown
## -0.2829967500
## color.fctrWhite
## -0.4615165875
## D.npnct08.log
## 0.5458798483
## prdline.my.fctriPad 1
## 2.8250473222
## prdline.my.fctriPad 2
## 1.1482785616
## prdline.my.fctriPad 3+
## 1.0968460885
## prdline.my.fctriPadAir
## 0.6047513818
## prdline.my.fctriPadmini
## 0.2100029069
## prdline.my.fctriPadmini 2+
## 0.6112190326
## D.nstopwrds.log
## 1.3081823019
## D.npnct16.log
## 2.4833324904
## D.npnct24.log
## -1.3003786797
## D.npnct06.log
## -5.0154566355
## D.npnct28.log
## -1.0203867837
## D.nuppr.log
## -0.0828883945
## D.nchrs.log
## -0.1278375544
## D.npnct12.log
## 0.4686035342
## D.npnct09.log
## -1.9516237091
## D.ndgts.log
## 0.7259517312
## D.nwrds.unq.log
## -0.3336914340
## D.terms.n.post.stem.log
## -0.2629347929
## D.terms.n.post.stop.log
## -0.2085973096
## D.npnct14.log
## -2.3774003067
## D.terms.n.post.stem
## -0.0932817113
## D.terms.n.post.stop
## -0.1638536562
## D.npnct05.log
## -3.5140577393
## condition.fctrFor parts or not working
## -0.0938135303
## condition.fctrManufacturer refurbished
## 0.4287285422
## condition.fctrNew
## -0.1515775865
## condition.fctrNew other (see details)
## 0.0581933230
## condition.fctrSeller refurbished
## -0.3999074103
## idseq.my
## 0.0005300156
## D.ratio.sum.TfIdf.nwrds
## 0.4934353587
## D.TfIdf.sum.stem.stop.Ratio
## -0.3128306550
## D.npnct15.log
## 1.4870458929
## startprice.diff
## -0.0056598648
## biddable
## 3.8431012027
## cellular.fctr1
## 0.0530357729
## cellular.fctrUnknown
## -0.1643065763
## carrier.fctrOther
## 2.3514697637
## carrier.fctrSprint
## 0.5549957289
## carrier.fctrT-Mobile
## -0.5656733259
## carrier.fctrUnknown
## -0.0431941180
## carrier.fctrVerizon
## 0.1876390074
## prdline.my.fctriPad 1:idseq.my
## -0.0001349832
## prdline.my.fctriPad 2:idseq.my
## -0.0011147109
## prdline.my.fctriPad 3+:idseq.my
## -0.0004146841
## prdline.my.fctriPadAir:idseq.my
## -0.0016035128
## prdline.my.fctriPadmini:idseq.my
## -0.0007005278
## prdline.my.fctriPadmini 2+:idseq.my
## -0.0001921516
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds
## -0.8695237648
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds
## 2.0958276654
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds
## -0.3714903075
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds
## -1.2486314268
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds
## -1.1477991985
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds
## -2.4047377301
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio
## -1.6431981838
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio
## 0.1354198500
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 2.1602136537
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio
## 1.0094472987
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio
## 0.4933747123
## prdline.my.fctriPad 1:D.npnct15.log
## 5.1248217418
## prdline.my.fctriPad 2:D.npnct15.log
## 5.3119048613
## prdline.my.fctriPad 3+:D.npnct15.log
## -2.1223652039
## prdline.my.fctriPadAir:D.npnct15.log
## 6.0437108665
## prdline.my.fctriPadmini:D.npnct15.log
## 9.2491556328
## prdline.my.fctriPadmini 2+:D.npnct15.log
## -3.5694815589
## prdline.my.fctriPad 1:D.npnct03.log
## 1.7626931778
## prdline.my.fctriPad 2:D.npnct03.log
## -0.0321411566
## prdline.my.fctriPad 3+:D.npnct03.log
## -4.5461616868
## prdline.my.fctriPadAir:D.npnct03.log
## 0.7658065373
## prdline.my.fctriPadmini:D.npnct03.log
## -0.1920338520
## prdline.my.fctriPadmini 2+:D.npnct03.log
## -3.5639761657
## startprice.diff:biddable
## -0.0135087297
## cellular.fctr1:carrier.fctrOther
## 2.3413483794
## cellular.fctr1:carrier.fctrSprint
## 0.5404536870
## cellular.fctr1:carrier.fctrT-Mobile
## -0.5745205114
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.1782125384
## cellular.fctr1:carrier.fctrVerizon
## 0.1837629663
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.4030982888
## prdline.my.fctriPad 1:.clusterid.fctr2
## 0.5912425706
## prdline.my.fctriPad 2:.clusterid.fctr2
## -0.4327392038
## prdline.my.fctriPad 3+:.clusterid.fctr2
## -0.1511747252
## prdline.my.fctriPadAir:.clusterid.fctr2
## -0.1007359331
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.2460841882
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 0.7467090013
## prdline.my.fctrUnknown:.clusterid.fctr3
## -1.5174303047
## prdline.my.fctriPad 1:.clusterid.fctr3
## -0.3015557736
## prdline.my.fctriPad 2:.clusterid.fctr3
## -1.8668231434
## prdline.my.fctriPad 3+:.clusterid.fctr3
## -0.5019116947
## prdline.my.fctriPadmini:.clusterid.fctr3
## 1.2576710189
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 1.2096547646
## prdline.my.fctriPad 1:.clusterid.fctr4
## -1.8692599083
## prdline.my.fctriPad 2:.clusterid.fctr4
## 2.0744185449
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 0.5675130539
## prdline.my.fctriPadmini:.clusterid.fctr4
## -0.1329778477
## prdline.my.fctriPad 3+:.clusterid.fctr5
## -1.1112420133
## prdline.my.fctriPadmini:.clusterid.fctr5
## -1.1371925380
## prdline.my.fctriPad 3+:.clusterid.fctr6
## -4.8416180291
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6507592
## 3 0.2 0.6973789
## 4 0.3 0.7535954
## 5 0.4 0.7890110
## 6 0.5 0.7961859
## 7 0.6 0.7965044
## 8 0.7 0.7364130
## 9 0.8 0.5509554
## 10 0.9 0.1132075
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.glmnet.N
## 1 N 492
## 2 Y 131
## sold.fctr.predict.All.Interact.X.glmnet.Y
## 1 32
## 2 319
## Prediction
## Reference N Y
## N 492 32
## Y 131 319
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.326489e-01 6.580424e-01 8.076954e-01 8.555717e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 4.994716e-84 1.641941e-14
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6449563
## 3 0.2 0.6910995
## 4 0.3 0.7378238
## 5 0.4 0.7606318
## 6 0.5 0.7630208
## 7 0.6 0.7578659
## 8 0.7 0.7184751
## 9 0.8 0.5291005
## 10 0.9 0.1013825
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.glmnet.N
## 1 N 410
## 2 Y 117
## sold.fctr.predict.All.Interact.X.glmnet.Y
## 1 65
## 2 293
## Prediction
## Reference N Y
## N 410 65
## Y 117 293
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.943503e-01 5.828499e-01 7.661969e-01 8.205280e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.494158e-57 1.565945e-04
## model_id model_method
## 1 All.Interact.X.glmnet glmnet
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 9.088 0.736
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8845377 0.6 0.7965044 0.7977651
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8076954 0.8555717 0.5903552 0.8510347
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7630208 0.7943503
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7661969 0.820528 0.5828499
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02063411 0.04268017
## label step_major step_minor bgn end elapsed
## 9 fit.models_1_glmnet 9 0 237.856 250.88 13.024
## 10 fit.models_1_rpart 10 0 250.881 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rpart"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.02 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.51111111 0 1.0000000
## 2 0.08666667 1 0.4888889
## 3 0.02000000 2 0.4022222
##
## Variable importance
## startprice.diff:biddable
## 36
## biddable
## 34
## startprice.diff
## 15
## idseq.my
## 7
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio
## 2
## prdline.my.fctriPad 1:idseq.my
## 2
## condition.fctrFor parts or not working
## 2
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio
## 1
## prdline.my.fctriPadAir:idseq.my
## 1
## condition.fctrNew
## 1
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## startprice.diff:biddable < -1.57273 to the right, improve= 99.97262, (0 missing)
## startprice.diff < 59.64413 to the right, improve= 48.01938, (0 missing)
## idseq.my < 905.5 to the right, improve= 38.91299, (0 missing)
## prdline.my.fctriPadAir:idseq.my < 869 to the right, improve= 12.82037, (0 missing)
## Surrogate splits:
## startprice.diff:biddable < 0.06166769 to the left, agree=0.829, adj=0.629, (0 split)
## idseq.my < 869 to the right, agree=0.636, adj=0.211, (0 split)
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio < 0.9972378 to the left, agree=0.566, adj=0.060, (0 split)
## prdline.my.fctriPad 1:idseq.my < 71 to the left, agree=0.564, adj=0.056, (0 split)
## condition.fctrFor parts or not working < 0.5 to the left, agree=0.563, adj=0.053, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations, complexity param=0.08666667
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
## left son=6 (135 obs) right son=7 (315 obs)
## Primary splits:
## startprice.diff < 68.91842 to the right, improve=61.714290, (0 missing)
## startprice.diff:biddable < 68.91842 to the right, improve=61.714290, (0 missing)
## idseq.my < 670.5 to the right, improve=15.110620, (0 missing)
## prdline.my.fctriPadmini 2+:idseq.my < 666.5 to the right, improve= 4.191636, (0 missing)
## condition.fctrNew < 0.5 to the right, improve= 3.467222, (0 missing)
## Surrogate splits:
## startprice.diff:biddable < 68.91842 to the right, agree=1.000, adj=1.000, (0 split)
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio < 0.9845277 to the right, agree=0.718, adj=0.059, (0 split)
## prdline.my.fctriPadAir:idseq.my < 741 to the right, agree=0.716, adj=0.052, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.713, adj=0.044, (0 split)
## prdline.my.fctriPadmini 2+ < 0.5 to the right, agree=0.709, adj=0.030, (0 split)
##
## Node number 6: 135 observations
## predicted class=N expected loss=0.3555556 P(node) =0.1386037
## class counts: 87 48
## probabilities: 0.644 0.356
##
## Node number 7: 315 observations
## predicted class=Y expected loss=0.07301587 P(node) =0.3234086
## class counts: 23 292
## probabilities: 0.073 0.927
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.53798768 0.46201232)
## 2) biddable< 0.5 524 110 N (0.79007634 0.20992366) *
## 3) biddable>=0.5 450 110 Y (0.24444444 0.75555556)
## 6) startprice.diff>=68.91842 135 48 N (0.64444444 0.35555556) *
## 7) startprice.diff< 68.91842 315 23 Y (0.07301587 0.92698413) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7555556
## 5 0.4 0.7633987
## 6 0.5 0.7633987
## 7 0.6 0.7633987
## 8 0.7 0.7633987
## 9 0.8 0.7633987
## 10 0.9 0.7633987
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rpart.N
## 1 N 501
## 2 Y 158
## sold.fctr.predict.All.Interact.X.no.rnorm.rpart.Y
## 1 23
## 2 292
## Prediction
## Reference N Y
## N 501 23
## Y 158 292
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.141684e-01 6.180889e-01 7.882906e-01 8.381305e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.530340e-73 2.277382e-23
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7528231
## 5 0.4 0.7420290
## 6 0.5 0.7420290
## 7 0.6 0.7420290
## 8 0.7 0.7420290
## 9 0.8 0.7420290
## 10 0.9 0.7420290
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rpart.N
## 1 N 388
## 2 Y 110
## sold.fctr.predict.All.Interact.X.no.rnorm.rpart.Y
## 1 87
## 2 300
## Prediction
## Reference N Y
## N 388 87
## Y 110 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.774011e-01 5.506630e-01 7.485294e-01 8.044124e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.956102e-50 1.170130e-01
## model_id model_method
## 1 All.Interact.X.no.rnorm.rpart rpart
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.854 0.093
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8243427 0.9 0.7633987 0.8018645
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7882906 0.8381305 0.5940341 0.8129705
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7528231 0.7774011
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7485294 0.8044124 0.550663
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01388821 0.02957203
## label step_major step_minor bgn end elapsed
## 10 fit.models_1_rpart 10 0 250.881 256.412 5.531
## 11 fit.models_1_rf 11 0 256.413 NA NA
## [1] "fitting model: All.Interact.X.no.rnorm.rf"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 71 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 140 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 140 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.8234218
## 3 0.2 0.9394572
## 4 0.3 0.9846827
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 0.9988877
## 8 0.7 0.9510490
## 9 0.8 0.8607595
## 10 0.9 0.7738420
## 11 1.0 0.1963928
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.N
## 1 N 524
## 2 Y NA
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.Y
## 1 NA
## 2 450
## Prediction
## Reference N Y
## N 524 0
## Y 0 450
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.962198e-01 1.000000e+00 5.379877e-01
## AccuracyPValue McnemarPValue
## 5.919016e-263 NaN
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6843501
## 3 0.2 0.7243781
## 4 0.3 0.7433628
## 5 0.4 0.7647768
## 6 0.5 0.7727856
## 7 0.6 0.7747253
## 8 0.7 0.7590188
## 9 0.8 0.7329377
## 10 0.9 0.6635071
## 11 1.0 0.1232877
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.N
## 1 N 439
## 2 Y 128
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.Y
## 1 36
## 2 282
## Prediction
## Reference N Y
## N 439 36
## Y 128 282
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.146893e-01 6.215582e-01 7.874927e-01 8.397715e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.806781e-67 1.195356e-12
## model_id model_method
## 1 All.Interact.X.no.rnorm.rf rf
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 20.893 7.009
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.7823457
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9962198 1 0.5584003 0.8559487
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.7747253 0.8146893
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7874927 0.8397715 0.6215582
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.006148854 0.01320333# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# model_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", model_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
model_id <- "csm"; indep_vars_vctr <- c(NULL
,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
,"prdline.my.fctr*biddable"
#,"prdline.my.fctr*startprice.log"
#,"prdline.my.fctr*startprice.diff"
#,"prdline.my.fctr*idseq.my"
,"prdline.my.fctr*condition.fctr"
,"prdline.my.fctr*D.terms.n.post.stop"
#,"prdline.my.fctr*D.terms.n.post.stem"
,"prdline.my.fctr*cellular.fctr"
# ,"<feat1>:<feat2>"
)
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
csm_mdl_id <- paste0(model_id, ".", method)
csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".",
method)]]); print(head(csm_featsimp_df))
}## [1] "fitting model: csm.glm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 639
## Warning: not plotting observations with leverage one:
## 639
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7858 -0.6818 -0.2582 0.6508 2.4690
##
## Coefficients: (17 not defined because of singularities)
## Estimate
## (Intercept) -1.900e+00
## `prdline.my.fctriPad 1` 8.143e-01
## `prdline.my.fctriPad 2` 3.003e-01
## `prdline.my.fctriPad 3+` 1.536e+00
## prdline.my.fctriPadAir 7.191e-01
## prdline.my.fctriPadmini 9.831e-01
## `prdline.my.fctriPadmini 2+` 8.573e-01
## biddable 2.399e+00
## `condition.fctrFor parts or not working` 1.060e+00
## `condition.fctrManufacturer refurbished` 1.088e+14
## condition.fctrNew -1.031e+00
## `condition.fctrNew other (see details)` 2.833e+00
## `condition.fctrSeller refurbished` 5.202e-01
## D.terms.n.post.stop 2.404e-02
## cellular.fctr1 -2.454e+00
## cellular.fctrUnknown -4.524e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.131e+00
## `prdline.my.fctriPad 1:.clusterid.fctr2` -5.541e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.618e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 2.816e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 3.566e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.171e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.949e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.127e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -2.100e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -1.001e-01
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 3.771e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -5.482e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 9.583e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 3.432e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -4.021e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 3.907e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.041e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.766e-02
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 6.110e-02
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -9.449e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -4.504e+15
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:biddable` 1.431e+00
## `prdline.my.fctriPad 2:biddable` 1.145e+00
## `prdline.my.fctriPad 3+:biddable` -4.215e-01
## `prdline.my.fctriPadAir:biddable` 6.005e-01
## `prdline.my.fctriPadmini:biddable` 2.015e-01
## `prdline.my.fctriPadmini 2+:biddable` -5.837e-01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.297e+00
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 5.342e-02
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.096e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -4.000e-01
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.106e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.199e+01
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -1.088e+14
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -1.088e+14
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -1.088e+14
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -1.088e+14
## `prdline.my.fctriPad 1:condition.fctrNew` -4.504e+15
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -7.190e-01
## `prdline.my.fctriPadAir:condition.fctrNew` 6.458e-02
## `prdline.my.fctriPadmini:condition.fctrNew` 5.439e-01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 6.983e-01
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -3.778e+00
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -4.504e+15
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -2.341e+00
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -1.724e+00
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -4.405e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -5.190e+00
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.968e+00
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -6.535e-01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -1.357e+00
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -2.834e-01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -2.047e+00
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -4.504e+15
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.124e-01
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -1.512e-01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -2.082e-01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -1.458e-01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -8.592e-02
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -1.662e-01
## `prdline.my.fctriPad 1:cellular.fctr1` 2.200e+00
## `prdline.my.fctriPad 2:cellular.fctr1` 1.831e+00
## `prdline.my.fctriPad 3+:cellular.fctr1` 2.267e+00
## `prdline.my.fctriPadAir:cellular.fctr1` 2.213e+00
## `prdline.my.fctriPadmini:cellular.fctr1` 2.708e+00
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 2.751e+00
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -3.392e-01
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -9.981e-01
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -4.377e-01
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 6.079e-01
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 4.677e-01
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 1.768e-01
## Std. Error
## (Intercept) 7.613e-01
## `prdline.my.fctriPad 1` 8.939e-01
## `prdline.my.fctriPad 2` 9.953e-01
## `prdline.my.fctriPad 3+` 8.824e-01
## prdline.my.fctriPadAir 8.557e-01
## prdline.my.fctriPadmini 8.621e-01
## `prdline.my.fctriPadmini 2+` 9.646e-01
## biddable 6.113e-01
## `condition.fctrFor parts or not working` 7.707e-01
## `condition.fctrManufacturer refurbished` 1.578e+14
## condition.fctrNew 9.190e-01
## `condition.fctrNew other (see details)` 1.504e+00
## `condition.fctrSeller refurbished` 1.076e+00
## D.terms.n.post.stop 8.799e-02
## cellular.fctr1 1.452e+00
## cellular.fctrUnknown 7.158e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 8.018e-01
## `prdline.my.fctriPad 1:.clusterid.fctr2` 1.409e+00
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.251e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 7.299e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 8.508e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 8.508e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.637e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` 9.530e-01
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.511e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.784e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 8.376e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` 8.448e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 1.049e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 1.278e+00
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.429e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.889e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 8.384e-01
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` 1.097e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 8.368e-01
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 1.125e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 2.740e+07
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:biddable` 9.144e-01
## `prdline.my.fctriPad 2:biddable` 8.818e-01
## `prdline.my.fctriPad 3+:biddable` 7.481e-01
## `prdline.my.fctriPadAir:biddable` 7.331e-01
## `prdline.my.fctriPadmini:biddable` 7.754e-01
## `prdline.my.fctriPadmini 2+:biddable` 8.069e-01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.642e+00
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 1.370e+00
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 9.934e-01
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 1.467e+00
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 9.623e-01
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 2.995e+00
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 1.578e+14
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.578e+14
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 1.578e+14
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 1.578e+14
## `prdline.my.fctriPad 1:condition.fctrNew` 3.875e+07
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.542e+00
## `prdline.my.fctriPadAir:condition.fctrNew` 1.039e+00
## `prdline.my.fctriPadmini:condition.fctrNew` 1.125e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.108e+00
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 2.996e+00
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 4.745e+07
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 1.745e+00
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 1.721e+00
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 2.065e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.965e+00
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.634e+00
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 1.476e+00
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 1.387e+00
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 1.466e+00
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.445e+00
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 4.745e+07
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 1.778e-01
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 1.663e-01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 1.176e-01
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 1.253e-01
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 1.244e-01
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 1.830e-01
## `prdline.my.fctriPad 1:cellular.fctr1` 1.588e+00
## `prdline.my.fctriPad 2:cellular.fctr1` 1.578e+00
## `prdline.my.fctriPad 3+:cellular.fctr1` 1.512e+00
## `prdline.my.fctriPadAir:cellular.fctr1` 1.511e+00
## `prdline.my.fctriPadmini:cellular.fctr1` 1.541e+00
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.589e+00
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 1.555e+00
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 1.592e+00
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 1.161e+00
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 1.488e+00
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 1.215e+00
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 1.095e+00
## z value
## (Intercept) -2.496e+00
## `prdline.my.fctriPad 1` 9.110e-01
## `prdline.my.fctriPad 2` 3.020e-01
## `prdline.my.fctriPad 3+` 1.740e+00
## prdline.my.fctriPadAir 8.400e-01
## prdline.my.fctriPadmini 1.140e+00
## `prdline.my.fctriPadmini 2+` 8.890e-01
## biddable 3.925e+00
## `condition.fctrFor parts or not working` 1.375e+00
## `condition.fctrManufacturer refurbished` 6.900e-01
## condition.fctrNew -1.122e+00
## `condition.fctrNew other (see details)` 1.883e+00
## `condition.fctrSeller refurbished` 4.840e-01
## D.terms.n.post.stop 2.730e-01
## cellular.fctr1 -1.690e+00
## cellular.fctrUnknown -6.320e-01
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.411e+00
## `prdline.my.fctriPad 1:.clusterid.fctr2` -3.930e-01
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.293e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 3.860e-01
## `prdline.my.fctriPadAir:.clusterid.fctr2` 4.190e-01
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.377e+00
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.191e+00
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.183e+00
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.389e+00
## `prdline.my.fctriPad 2:.clusterid.fctr3` -5.600e-02
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 4.500e-01
## `prdline.my.fctriPadAir:.clusterid.fctr3` -6.490e-01
## `prdline.my.fctriPadmini:.clusterid.fctr3` 9.130e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 2.690e-01
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.813e+00
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.068e+00
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.242e+00
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` -3.400e-02
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 7.300e-02
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` -8.400e-01
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.644e+08
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:biddable` 1.565e+00
## `prdline.my.fctriPad 2:biddable` 1.299e+00
## `prdline.my.fctriPad 3+:biddable` -5.630e-01
## `prdline.my.fctriPadAir:biddable` 8.190e-01
## `prdline.my.fctriPadmini:biddable` 2.600e-01
## `prdline.my.fctriPadmini 2+:biddable` -7.230e-01
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 7.900e-01
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 3.900e-02
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -1.103e+00
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -2.730e-01
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -2.189e+00
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -4.004e+00
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -6.900e-01
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` -6.900e-01
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -6.900e-01
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -6.900e-01
## `prdline.my.fctriPad 1:condition.fctrNew` -1.162e+08
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` -4.660e-01
## `prdline.my.fctriPadAir:condition.fctrNew` 6.200e-02
## `prdline.my.fctriPadmini:condition.fctrNew` 4.840e-01
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 6.300e-01
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -1.261e+00
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -9.491e+07
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` -1.342e+00
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` -1.002e+00
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -2.133e+00
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -2.641e+00
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.205e+00
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -4.430e-01
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -9.790e-01
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` -1.930e-01
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -1.416e+00
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -9.491e+07
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 6.320e-01
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -9.090e-01
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.771e+00
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -1.164e+00
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -6.910e-01
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -9.080e-01
## `prdline.my.fctriPad 1:cellular.fctr1` 1.385e+00
## `prdline.my.fctriPad 2:cellular.fctr1` 1.161e+00
## `prdline.my.fctriPad 3+:cellular.fctr1` 1.499e+00
## `prdline.my.fctriPadAir:cellular.fctr1` 1.465e+00
## `prdline.my.fctriPadmini:cellular.fctr1` 1.758e+00
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.731e+00
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -2.180e-01
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -6.270e-01
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -3.770e-01
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 4.080e-01
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 3.850e-01
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 1.610e-01
## Pr(>|z|)
## (Intercept) 0.01255
## `prdline.my.fctriPad 1` 0.36235
## `prdline.my.fctriPad 2` 0.76289
## `prdline.my.fctriPad 3+` 0.08183
## prdline.my.fctriPadAir 0.40066
## prdline.my.fctriPadmini 0.25415
## `prdline.my.fctriPadmini 2+` 0.37412
## biddable 8.66e-05
## `condition.fctrFor parts or not working` 0.16919
## `condition.fctrManufacturer refurbished` 0.49039
## condition.fctrNew 0.26172
## `condition.fctrNew other (see details)` 0.05970
## `condition.fctrSeller refurbished` 0.62872
## D.terms.n.post.stop 0.78472
## cellular.fctr1 0.09099
## cellular.fctrUnknown 0.52740
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.15826
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.69414
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.19613
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.69960
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.67511
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.16866
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.23360
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.23689
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.16471
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.95523
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.65254
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.51643
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.36105
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.78826
## `prdline.my.fctrUnknown:.clusterid.fctr4` NA
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.00491
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.03862
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.21436
## `prdline.my.fctriPadAir:.clusterid.fctr4` NA
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.97263
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` NA
## `prdline.my.fctrUnknown:.clusterid.fctr5` NA
## `prdline.my.fctriPad 1:.clusterid.fctr5` NA
## `prdline.my.fctriPad 2:.clusterid.fctr5` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.94180
## `prdline.my.fctriPadAir:.clusterid.fctr5` NA
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.40098
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` NA
## `prdline.my.fctrUnknown:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:.clusterid.fctr6` NA
## `prdline.my.fctriPad 2:.clusterid.fctr6` NA
## `prdline.my.fctriPad 3+:.clusterid.fctr6` < 2e-16
## `prdline.my.fctriPadAir:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini:.clusterid.fctr6` NA
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` NA
## `prdline.my.fctriPad 1:biddable` 0.11759
## `prdline.my.fctriPad 2:biddable` 0.19394
## `prdline.my.fctriPad 3+:biddable` 0.57312
## `prdline.my.fctriPadAir:biddable` 0.41272
## `prdline.my.fctriPadmini:biddable` 0.79492
## `prdline.my.fctriPadmini 2+:biddable` 0.46948
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.42955
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.96889
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.27009
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.78506
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.02861
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 6.23e-05
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` NA
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.49039
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.49039
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.49039
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.49039
## `prdline.my.fctriPad 1:condition.fctrNew` < 2e-16
## `prdline.my.fctriPad 2:condition.fctrNew` NA
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.64103
## `prdline.my.fctriPadAir:condition.fctrNew` 0.95046
## `prdline.my.fctriPadmini:condition.fctrNew` 0.62869
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.52849
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.20724
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` < 2e-16
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.17975
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.31652
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.03290
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.00827
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.22825
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.65797
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.32773
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.84665
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.15674
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` < 2e-16
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.52720
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.36314
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.07661
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.24462
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.48960
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.36391
## `prdline.my.fctriPad 1:cellular.fctr1` 0.16593
## `prdline.my.fctriPad 2:cellular.fctr1` 0.24582
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.13391
## `prdline.my.fctriPadAir:cellular.fctr1` 0.14289
## `prdline.my.fctriPadmini:cellular.fctr1` 0.07878
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.08347
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.82735
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.53060
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.70625
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.68296
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.70019
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.87178
##
## (Intercept) *
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+` .
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew
## `condition.fctrNew other (see details)` .
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1 .
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2`
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` **
## `prdline.my.fctriPad 2:.clusterid.fctr4` *
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctrUnknown:.clusterid.fctr6`
## `prdline.my.fctriPad 1:.clusterid.fctr6`
## `prdline.my.fctriPad 2:.clusterid.fctr6`
## `prdline.my.fctriPad 3+:.clusterid.fctr6` ***
## `prdline.my.fctriPadAir:.clusterid.fctr6`
## `prdline.my.fctriPadmini:.clusterid.fctr6`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6`
## `prdline.my.fctriPad 1:biddable`
## `prdline.my.fctriPad 2:biddable`
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable`
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` *
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` ***
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew` ***
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` ***
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` *
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` **
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` ***
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` .
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1` .
## `prdline.my.fctriPadmini 2+:cellular.fctr1` .
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown`
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 885.99 on 886 degrees of freedom
## AIC: 1062
##
## Number of Fisher Scoring iterations: 25
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6815385
## 3 0.2 0.7357331
## 4 0.3 0.7770961
## 5 0.4 0.7768240
## 6 0.5 0.7752809
## 7 0.6 0.7623529
## 8 0.7 0.7310167
## 9 0.8 0.6447932
## 10 0.9 0.2953271
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.glm.N sold.fctr.predict.csm.glm.Y
## 1 N 376 148
## 2 Y 70 380
## Prediction
## Reference N Y
## N 376 148
## Y 70 380
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.761807e-01 5.552064e-01 7.486774e-01 8.020028e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 8.551925e-54 1.837200e-07
## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6465517
## 3 0.2 0.6802326
## 4 0.3 0.7013575
## 5 0.4 0.7148014
## 6 0.5 0.7094340
## 7 0.6 0.7032680
## 8 0.7 0.6373938
## 9 0.8 0.5665635
## 10 0.9 0.2545455
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.glm.N sold.fctr.predict.csm.glm.Y
## 1 N 351 124
## 2 Y 113 297
## Prediction
## Reference N Y
## N 351 124
## Y 113 297
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.322034e-01 4.624886e-01 7.017229e-01 7.611290e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 5.368860e-33 5.159701e-01
## model_id model_method
## 1 csm.glm glm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.892 0.434
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8645971 0.3 0.7770961 0.7310098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7486774 0.8020028 0.4584371 0.7577818
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7148014 0.7322034
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7017229 0.761129 0.4624886 1061.995
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01871693 0.04314557
## importance
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 1.000000e+02
## `prdline.my.fctriPad 1:condition.fctrNew` 7.070959e+01
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 5.773499e+01
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 5.773496e+01
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 2.414836e-06
## biddable 2.367024e-06
## [1] "fitting model: csm.bayesglm"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5095 -0.7012 -0.3496 0.6620 2.4254
##
## Coefficients:
## Estimate
## (Intercept) -1.508512
## `prdline.my.fctriPad 1` 0.463383
## `prdline.my.fctriPad 2` 0.060657
## `prdline.my.fctriPad 3+` 1.074380
## prdline.my.fctriPadAir 0.377256
## prdline.my.fctriPadmini 0.604061
## `prdline.my.fctriPadmini 2+` 0.393566
## biddable 2.056725
## `condition.fctrFor parts or not working` 0.615225
## `condition.fctrManufacturer refurbished` -0.076083
## condition.fctrNew -1.125439
## `condition.fctrNew other (see details)` 0.517524
## `condition.fctrSeller refurbished` 0.015575
## D.terms.n.post.stop -0.003836
## cellular.fctr1 -0.701578
## cellular.fctrUnknown -0.311681
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.944240
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.018028
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.017765
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.228491
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.268898
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.940611
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.057369
## `prdline.my.fctrUnknown:.clusterid.fctr3` -0.894932
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.219411
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.556292
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.297080
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.480489
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.784650
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.136721
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.938293
## `prdline.my.fctriPad 2:.clusterid.fctr4` 3.105465
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.904838
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.244613
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.012033
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.821816
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -1.413633
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.000000
## `prdline.my.fctriPad 1:biddable` 1.394635
## `prdline.my.fctriPad 2:biddable` 1.312908
## `prdline.my.fctriPad 3+:biddable` -0.043244
## `prdline.my.fctriPadAir:biddable` 0.877863
## `prdline.my.fctriPadmini:biddable` 0.414435
## `prdline.my.fctriPadmini 2+:biddable` -0.209169
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.625643
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.257825
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.591202
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.186294
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.440430
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -1.258249
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.000000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.000000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.210818
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.377732
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -0.453904
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.674377
## `prdline.my.fctriPad 1:condition.fctrNew` -1.955158
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.444166
## `prdline.my.fctriPadAir:condition.fctrNew` 0.162938
## `prdline.my.fctriPadmini:condition.fctrNew` 0.625985
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.842047
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.626391
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.995009
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.010610
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.457803
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.505134
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -2.260569
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.854995
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.093928
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.714342
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.150427
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -1.257000
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -2.038421
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.078413
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.065103
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -0.167531
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -0.107755
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.044040
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.072363
## `prdline.my.fctriPad 1:cellular.fctr1` 0.405270
## `prdline.my.fctriPad 2:cellular.fctr1` 0.042205
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.540558
## `prdline.my.fctriPadAir:cellular.fctr1` 0.422384
## `prdline.my.fctriPadmini:cellular.fctr1` 0.908276
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.890094
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -0.376446
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.790415
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.484768
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.239034
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.208358
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.029532
## Std. Error
## (Intercept) 0.460297
## `prdline.my.fctriPad 1` 0.600087
## `prdline.my.fctriPad 2` 0.661639
## `prdline.my.fctriPad 3+` 0.595670
## prdline.my.fctriPadAir 0.564969
## prdline.my.fctriPadmini 0.568108
## `prdline.my.fctriPadmini 2+` 0.664848
## biddable 0.420601
## `condition.fctrFor parts or not working` 0.540775
## `condition.fctrManufacturer refurbished` 1.042265
## condition.fctrNew 0.605326
## `condition.fctrNew other (see details)` 0.746275
## `condition.fctrSeller refurbished` 0.667977
## D.terms.n.post.stop 0.068715
## cellular.fctr1 0.603735
## cellular.fctrUnknown 0.459164
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.651548
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.958051
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.860839
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.634082
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.727854
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.705941
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 1.179868
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.787226
## `prdline.my.fctriPad 1:.clusterid.fctr3` 1.011018
## `prdline.my.fctriPad 2:.clusterid.fctr3` 1.196935
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.726823
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.727995
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.852444
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.973812
## `prdline.my.fctrUnknown:.clusterid.fctr4` 2.500000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 1.003950
## `prdline.my.fctriPad 2:.clusterid.fctr4` 1.195250
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.731258
## `prdline.my.fctriPadAir:.clusterid.fctr4` 2.500000
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.879112
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 2.500000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.726767
## `prdline.my.fctriPadAir:.clusterid.fctr5` 2.500000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.927142
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 2.500000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 1.536630
## `prdline.my.fctriPadAir:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 2.500000
## `prdline.my.fctriPad 1:biddable` 0.674992
## `prdline.my.fctriPad 2:biddable` 0.665157
## `prdline.my.fctriPad 3+:biddable` 0.565566
## `prdline.my.fctriPadAir:biddable` 0.557964
## `prdline.my.fctriPadmini:biddable` 0.582688
## `prdline.my.fctriPadmini 2+:biddable` 0.619813
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.139607
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 1.015844
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.760329
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.983406
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.731036
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 1.625974
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 2.500000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 2.500000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 1.215864
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.271817
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 1.211499
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 1.831739
## `prdline.my.fctriPad 1:condition.fctrNew` 1.689442
## `prdline.my.fctriPad 2:condition.fctrNew` 2.500000
## `prdline.my.fctriPad 3+:condition.fctrNew` 1.109556
## `prdline.my.fctriPadAir:condition.fctrNew` 0.728326
## `prdline.my.fctriPadmini:condition.fctrNew` 0.805481
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.783553
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 1.590404
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 1.685738
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.998661
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.981804
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 1.245644
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 1.199084
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.126210
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.992255
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.960704
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 1.015081
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 1.008867
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 1.675167
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.125658
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.118527
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.095145
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.101723
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.099149
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.139056
## `prdline.my.fctriPad 1:cellular.fctr1` 0.776112
## `prdline.my.fctriPad 2:cellular.fctr1` 0.776217
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.699777
## `prdline.my.fctriPadAir:cellular.fctr1` 0.698589
## `prdline.my.fctriPadmini:cellular.fctr1` 0.739887
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.805923
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 1.120308
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 1.152360
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.889867
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 1.129040
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.920104
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.840295
## z value
## (Intercept) -3.277
## `prdline.my.fctriPad 1` 0.772
## `prdline.my.fctriPad 2` 0.092
## `prdline.my.fctriPad 3+` 1.804
## prdline.my.fctriPadAir 0.668
## prdline.my.fctriPadmini 1.063
## `prdline.my.fctriPadmini 2+` 0.592
## biddable 4.890
## `condition.fctrFor parts or not working` 1.138
## `condition.fctrManufacturer refurbished` -0.073
## condition.fctrNew -1.859
## `condition.fctrNew other (see details)` 0.693
## `condition.fctrSeller refurbished` 0.023
## D.terms.n.post.stop -0.056
## cellular.fctr1 -1.162
## cellular.fctrUnknown -0.679
## `prdline.my.fctrUnknown:.clusterid.fctr2` 1.449
## `prdline.my.fctriPad 1:.clusterid.fctr2` -0.019
## `prdline.my.fctriPad 2:.clusterid.fctr2` 1.182
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.360
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.369
## `prdline.my.fctriPadmini:.clusterid.fctr2` 1.332
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.896
## `prdline.my.fctrUnknown:.clusterid.fctr3` -1.137
## `prdline.my.fctriPad 1:.clusterid.fctr3` -1.206
## `prdline.my.fctriPad 2:.clusterid.fctr3` -0.465
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.409
## `prdline.my.fctriPadAir:.clusterid.fctr3` -0.660
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.920
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` -0.140
## `prdline.my.fctrUnknown:.clusterid.fctr4` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr4` -2.927
## `prdline.my.fctriPad 2:.clusterid.fctr4` 2.598
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 1.237
## `prdline.my.fctriPadAir:.clusterid.fctr4` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr4` -0.278
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 0.000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.017
## `prdline.my.fctriPadAir:.clusterid.fctr5` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr5` -0.886
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 0.000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 0.000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 0.000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 0.000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` -0.920
## `prdline.my.fctriPadAir:.clusterid.fctr6` 0.000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 0.000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 0.000
## `prdline.my.fctriPad 1:biddable` 2.066
## `prdline.my.fctriPad 2:biddable` 1.974
## `prdline.my.fctriPad 3+:biddable` -0.076
## `prdline.my.fctriPadAir:biddable` 1.573
## `prdline.my.fctriPadmini:biddable` 0.711
## `prdline.my.fctriPadmini 2+:biddable` -0.337
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 1.426
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.254
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` -0.778
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` -0.189
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` -1.970
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` -0.774
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 0.000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 0.000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` -0.173
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 1.083
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` -0.375
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` -0.368
## `prdline.my.fctriPad 1:condition.fctrNew` -1.157
## `prdline.my.fctriPad 2:condition.fctrNew` 0.000
## `prdline.my.fctriPad 3+:condition.fctrNew` -0.400
## `prdline.my.fctriPadAir:condition.fctrNew` 0.224
## `prdline.my.fctriPadmini:condition.fctrNew` 0.777
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 1.075
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` -0.394
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` -0.590
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.011
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.466
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` -1.208
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` -1.885
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 1.647
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` -0.095
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` -0.744
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.148
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` -1.246
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` -1.217
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.624
## `prdline.my.fctriPad 2:D.terms.n.post.stop` -0.549
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` -1.761
## `prdline.my.fctriPadAir:D.terms.n.post.stop` -1.059
## `prdline.my.fctriPadmini:D.terms.n.post.stop` -0.444
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` -0.520
## `prdline.my.fctriPad 1:cellular.fctr1` 0.522
## `prdline.my.fctriPad 2:cellular.fctr1` 0.054
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.772
## `prdline.my.fctriPadAir:cellular.fctr1` 0.605
## `prdline.my.fctriPadmini:cellular.fctr1` 1.228
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 1.104
## `prdline.my.fctriPad 1:cellular.fctrUnknown` -0.336
## `prdline.my.fctriPad 2:cellular.fctrUnknown` -0.686
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` -0.545
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.212
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.226
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.035
## Pr(>|z|)
## (Intercept) 0.00105
## `prdline.my.fctriPad 1` 0.44000
## `prdline.my.fctriPad 2` 0.92695
## `prdline.my.fctriPad 3+` 0.07129
## prdline.my.fctriPadAir 0.50430
## prdline.my.fctriPadmini 0.28765
## `prdline.my.fctriPadmini 2+` 0.55388
## biddable 1.01e-06
## `condition.fctrFor parts or not working` 0.25526
## `condition.fctrManufacturer refurbished` 0.94181
## condition.fctrNew 0.06299
## `condition.fctrNew other (see details)` 0.48801
## `condition.fctrSeller refurbished` 0.98140
## D.terms.n.post.stop 0.95548
## cellular.fctr1 0.24521
## cellular.fctrUnknown 0.49726
## `prdline.my.fctrUnknown:.clusterid.fctr2` 0.14727
## `prdline.my.fctriPad 1:.clusterid.fctr2` 0.98499
## `prdline.my.fctriPad 2:.clusterid.fctr2` 0.23709
## `prdline.my.fctriPad 3+:.clusterid.fctr2` 0.71859
## `prdline.my.fctriPadAir:.clusterid.fctr2` 0.71180
## `prdline.my.fctriPadmini:.clusterid.fctr2` 0.18272
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2` 0.37016
## `prdline.my.fctrUnknown:.clusterid.fctr3` 0.25561
## `prdline.my.fctriPad 1:.clusterid.fctr3` 0.22777
## `prdline.my.fctriPad 2:.clusterid.fctr3` 0.64210
## `prdline.my.fctriPad 3+:.clusterid.fctr3` 0.68273
## `prdline.my.fctriPadAir:.clusterid.fctr3` 0.50924
## `prdline.my.fctriPadmini:.clusterid.fctr3` 0.35733
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3` 0.88835
## `prdline.my.fctrUnknown:.clusterid.fctr4` 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr4` 0.00343
## `prdline.my.fctriPad 2:.clusterid.fctr4` 0.00937
## `prdline.my.fctriPad 3+:.clusterid.fctr4` 0.21595
## `prdline.my.fctriPadAir:.clusterid.fctr4` 1.00000
## `prdline.my.fctriPadmini:.clusterid.fctr4` 0.78082
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4` 1.00000
## `prdline.my.fctrUnknown:.clusterid.fctr5` 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr5` 1.00000
## `prdline.my.fctriPad 2:.clusterid.fctr5` 1.00000
## `prdline.my.fctriPad 3+:.clusterid.fctr5` 0.98679
## `prdline.my.fctriPadAir:.clusterid.fctr5` 1.00000
## `prdline.my.fctriPadmini:.clusterid.fctr5` 0.37540
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5` 1.00000
## `prdline.my.fctrUnknown:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPad 1:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPad 2:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPad 3+:.clusterid.fctr6` 0.35760
## `prdline.my.fctriPadAir:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPadmini:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6` 1.00000
## `prdline.my.fctriPad 1:biddable` 0.03881
## `prdline.my.fctriPad 2:biddable` 0.04840
## `prdline.my.fctriPad 3+:biddable` 0.93905
## `prdline.my.fctriPadAir:biddable` 0.11564
## `prdline.my.fctriPadmini:biddable` 0.47693
## `prdline.my.fctriPadmini 2+:biddable` 0.73576
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working` 0.15373
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working` 0.79965
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working` 0.43683
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working` 0.84975
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` 0.04879
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working` 0.43902
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished` 1.00000
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished` 1.00000
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished` 0.86235
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished` 0.27868
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished` 0.70791
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished` 0.71275
## `prdline.my.fctriPad 1:condition.fctrNew` 0.24716
## `prdline.my.fctriPad 2:condition.fctrNew` 1.00000
## `prdline.my.fctriPad 3+:condition.fctrNew` 0.68893
## `prdline.my.fctriPadAir:condition.fctrNew` 0.82298
## `prdline.my.fctriPadmini:condition.fctrNew` 0.43707
## `prdline.my.fctriPadmini 2+:condition.fctrNew` 0.28253
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)` 0.69369
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)` 0.55502
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)` 0.99152
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)` 0.64101
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)` 0.22692
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` 0.05940
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` 0.09953
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished` 0.92458
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished` 0.45714
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished` 0.88219
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished` 0.21278
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished` 0.22366
## `prdline.my.fctriPad 1:D.terms.n.post.stop` 0.53261
## `prdline.my.fctriPad 2:D.terms.n.post.stop` 0.58283
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` 0.07827
## `prdline.my.fctriPadAir:D.terms.n.post.stop` 0.28946
## `prdline.my.fctriPadmini:D.terms.n.post.stop` 0.65691
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop` 0.60279
## `prdline.my.fctriPad 1:cellular.fctr1` 0.60155
## `prdline.my.fctriPad 2:cellular.fctr1` 0.95664
## `prdline.my.fctriPad 3+:cellular.fctr1` 0.43983
## `prdline.my.fctriPadAir:cellular.fctr1` 0.54543
## `prdline.my.fctriPadmini:cellular.fctr1` 0.21960
## `prdline.my.fctriPadmini 2+:cellular.fctr1` 0.26940
## `prdline.my.fctriPad 1:cellular.fctrUnknown` 0.73686
## `prdline.my.fctriPad 2:cellular.fctrUnknown` 0.49277
## `prdline.my.fctriPad 3+:cellular.fctrUnknown` 0.58592
## `prdline.my.fctriPadAir:cellular.fctrUnknown` 0.83233
## `prdline.my.fctriPadmini:cellular.fctrUnknown` 0.82085
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown` 0.97196
##
## (Intercept) **
## `prdline.my.fctriPad 1`
## `prdline.my.fctriPad 2`
## `prdline.my.fctriPad 3+` .
## prdline.my.fctriPadAir
## prdline.my.fctriPadmini
## `prdline.my.fctriPadmini 2+`
## biddable ***
## `condition.fctrFor parts or not working`
## `condition.fctrManufacturer refurbished`
## condition.fctrNew .
## `condition.fctrNew other (see details)`
## `condition.fctrSeller refurbished`
## D.terms.n.post.stop
## cellular.fctr1
## cellular.fctrUnknown
## `prdline.my.fctrUnknown:.clusterid.fctr2`
## `prdline.my.fctriPad 1:.clusterid.fctr2`
## `prdline.my.fctriPad 2:.clusterid.fctr2`
## `prdline.my.fctriPad 3+:.clusterid.fctr2`
## `prdline.my.fctriPadAir:.clusterid.fctr2`
## `prdline.my.fctriPadmini:.clusterid.fctr2`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr2`
## `prdline.my.fctrUnknown:.clusterid.fctr3`
## `prdline.my.fctriPad 1:.clusterid.fctr3`
## `prdline.my.fctriPad 2:.clusterid.fctr3`
## `prdline.my.fctriPad 3+:.clusterid.fctr3`
## `prdline.my.fctriPadAir:.clusterid.fctr3`
## `prdline.my.fctriPadmini:.clusterid.fctr3`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr3`
## `prdline.my.fctrUnknown:.clusterid.fctr4`
## `prdline.my.fctriPad 1:.clusterid.fctr4` **
## `prdline.my.fctriPad 2:.clusterid.fctr4` **
## `prdline.my.fctriPad 3+:.clusterid.fctr4`
## `prdline.my.fctriPadAir:.clusterid.fctr4`
## `prdline.my.fctriPadmini:.clusterid.fctr4`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr4`
## `prdline.my.fctrUnknown:.clusterid.fctr5`
## `prdline.my.fctriPad 1:.clusterid.fctr5`
## `prdline.my.fctriPad 2:.clusterid.fctr5`
## `prdline.my.fctriPad 3+:.clusterid.fctr5`
## `prdline.my.fctriPadAir:.clusterid.fctr5`
## `prdline.my.fctriPadmini:.clusterid.fctr5`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr5`
## `prdline.my.fctrUnknown:.clusterid.fctr6`
## `prdline.my.fctriPad 1:.clusterid.fctr6`
## `prdline.my.fctriPad 2:.clusterid.fctr6`
## `prdline.my.fctriPad 3+:.clusterid.fctr6`
## `prdline.my.fctriPadAir:.clusterid.fctr6`
## `prdline.my.fctriPadmini:.clusterid.fctr6`
## `prdline.my.fctriPadmini 2+:.clusterid.fctr6`
## `prdline.my.fctriPad 1:biddable` *
## `prdline.my.fctriPad 2:biddable` *
## `prdline.my.fctriPad 3+:biddable`
## `prdline.my.fctriPadAir:biddable`
## `prdline.my.fctriPadmini:biddable`
## `prdline.my.fctriPadmini 2+:biddable`
## `prdline.my.fctriPad 1:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 2:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 3+:condition.fctrFor parts or not working`
## `prdline.my.fctriPadAir:condition.fctrFor parts or not working`
## `prdline.my.fctriPadmini:condition.fctrFor parts or not working` *
## `prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working`
## `prdline.my.fctriPad 1:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 2:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadAir:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished`
## `prdline.my.fctriPad 1:condition.fctrNew`
## `prdline.my.fctriPad 2:condition.fctrNew`
## `prdline.my.fctriPad 3+:condition.fctrNew`
## `prdline.my.fctriPadAir:condition.fctrNew`
## `prdline.my.fctriPadmini:condition.fctrNew`
## `prdline.my.fctriPadmini 2+:condition.fctrNew`
## `prdline.my.fctriPad 1:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 2:condition.fctrNew other (see details)`
## `prdline.my.fctriPad 3+:condition.fctrNew other (see details)`
## `prdline.my.fctriPadAir:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini:condition.fctrNew other (see details)`
## `prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)` .
## `prdline.my.fctriPad 1:condition.fctrSeller refurbished` .
## `prdline.my.fctriPad 2:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 3+:condition.fctrSeller refurbished`
## `prdline.my.fctriPadAir:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini:condition.fctrSeller refurbished`
## `prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished`
## `prdline.my.fctriPad 1:D.terms.n.post.stop`
## `prdline.my.fctriPad 2:D.terms.n.post.stop`
## `prdline.my.fctriPad 3+:D.terms.n.post.stop` .
## `prdline.my.fctriPadAir:D.terms.n.post.stop`
## `prdline.my.fctriPadmini:D.terms.n.post.stop`
## `prdline.my.fctriPadmini 2+:D.terms.n.post.stop`
## `prdline.my.fctriPad 1:cellular.fctr1`
## `prdline.my.fctriPad 2:cellular.fctr1`
## `prdline.my.fctriPad 3+:cellular.fctr1`
## `prdline.my.fctriPadAir:cellular.fctr1`
## `prdline.my.fctriPadmini:cellular.fctr1`
## `prdline.my.fctriPadmini 2+:cellular.fctr1`
## `prdline.my.fctriPad 1:cellular.fctrUnknown`
## `prdline.my.fctriPad 2:cellular.fctrUnknown`
## `prdline.my.fctriPad 3+:cellular.fctrUnknown`
## `prdline.my.fctriPadAir:cellular.fctrUnknown`
## `prdline.my.fctriPadmini:cellular.fctrUnknown`
## `prdline.my.fctriPadmini 2+:cellular.fctrUnknown`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1344.62 on 973 degrees of freedom
## Residual deviance: 896.21 on 869 degrees of freedom
## AIC: 1106.2
##
## Number of Fisher Scoring iterations: 16
##
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6732075
## 3 0.2 0.7394808
## 4 0.3 0.7742594
## 5 0.4 0.7789934
## 6 0.5 0.7683616
## 7 0.6 0.7544484
## 8 0.7 0.7244094
## 9 0.8 0.6160584
## 10 0.9 0.2509653
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.bayesglm.N
## 1 N 416
## 2 Y 94
## sold.fctr.predict.csm.bayesglm.Y
## 1 108
## 2 356
## Prediction
## Reference N Y
## N 416 108
## Y 94 356
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.926078e-01 5.837219e-01 7.657655e-01 8.176677e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 1.015605e-61 3.603613e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6583748
## 3 0.2 0.6894531
## 4 0.3 0.7144482
## 5 0.4 0.7310513
## 6 0.5 0.7263556
## 7 0.6 0.7043364
## 8 0.7 0.6209913
## 9 0.8 0.5655608
## 10 0.9 0.1802575
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.bayesglm.N
## 1 N 366
## 2 Y 111
## sold.fctr.predict.csm.bayesglm.Y
## 1 109
## 2 299
## Prediction
## Reference N Y
## N 366 109
## Y 111 299
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.514124e-01 4.999615e-01 7.215645e-01 7.795759e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.010199e-39 9.462474e-01
## model_id model_method
## 1 csm.bayesglm bayesglm
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.058 0.424
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.862676 0.4 0.7789934 0.7576923
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7657655 0.8176677 0.5127898 0.7749705
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7310513 0.7514124
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7215645 0.7795759 0.4999615 1106.207
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008104349 0.01872738
## N importance
## biddable 100.000000 100.000000
## prdline.my.fctr 34.103141 34.103141
## .clusterid.fctr 10.507475 10.507475
## D.terms.n.post.stop 6.216049 6.216049
## cellular.fctr 5.046058 5.046058
## condition.fctr 0.000000 0.000000
## [1] "fitting model: csm.glmnet"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0544 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 95 -none- numeric
## beta 9880 dgCMatrix S4
## df 95 -none- numeric
## dim 2 -none- numeric
## lambda 95 -none- numeric
## dev.ratio 95 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 104 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -1.007559311
## biddable
## 1.779236846
## condition.fctrNew
## -0.166509715
## cellular.fctrUnknown
## -0.048814838
## prdline.my.fctriPad 2:.clusterid.fctr4
## 0.348938535
## prdline.my.fctriPad 1:biddable
## 0.283880312
## prdline.my.fctriPad 2:biddable
## 0.258093898
## prdline.my.fctriPad 1:condition.fctrFor parts or not working
## 0.598206083
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -0.005815295
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -1.84845980
## prdline.my.fctriPad 1
## 0.76424893
## prdline.my.fctriPad 2
## 0.24371339
## prdline.my.fctriPad 3+
## 1.49877412
## prdline.my.fctriPadAir
## 0.68168898
## prdline.my.fctriPadmini
## 0.94481896
## prdline.my.fctriPadmini 2+
## 0.84960693
## biddable
## 2.31363756
## condition.fctrFor parts or not working
## 1.05296785
## condition.fctrNew
## -1.05318838
## condition.fctrNew other (see details)
## 2.58918213
## condition.fctrSeller refurbished
## 0.52474944
## D.terms.n.post.stop
## 0.01926081
## cellular.fctr1
## -2.11025050
## cellular.fctrUnknown
## -0.40150057
## prdline.my.fctrUnknown:.clusterid.fctr2
## 1.08105059
## prdline.my.fctriPad 1:.clusterid.fctr2
## -0.59766521
## prdline.my.fctriPad 2:.clusterid.fctr2
## 1.57803269
## prdline.my.fctriPad 3+:.clusterid.fctr2
## 0.26715744
## prdline.my.fctriPadAir:.clusterid.fctr2
## 0.37320221
## prdline.my.fctriPadmini:.clusterid.fctr2
## 1.14938203
## prdline.my.fctriPadmini 2+:.clusterid.fctr2
## 1.83740653
## prdline.my.fctrUnknown:.clusterid.fctr3
## -1.11129890
## prdline.my.fctriPad 1:.clusterid.fctr3
## -2.05930359
## prdline.my.fctriPad 2:.clusterid.fctr3
## -0.14138251
## prdline.my.fctriPad 3+:.clusterid.fctr3
## 0.35479063
## prdline.my.fctriPadAir:.clusterid.fctr3
## -0.52529553
## prdline.my.fctriPadmini:.clusterid.fctr3
## 0.96421664
## prdline.my.fctriPadmini 2+:.clusterid.fctr3
## 0.06399376
## prdline.my.fctriPad 1:.clusterid.fctr4
## -3.98588144
## prdline.my.fctriPad 2:.clusterid.fctr4
## 4.06349538
## prdline.my.fctriPad 3+:.clusterid.fctr4
## 1.01897070
## prdline.my.fctriPadmini:.clusterid.fctr4
## -0.05059401
## prdline.my.fctriPad 3+:.clusterid.fctr5
## 0.04042165
## prdline.my.fctriPadmini:.clusterid.fctr5
## -0.94233411
## prdline.my.fctriPad 3+:.clusterid.fctr6
## -5.51692804
## prdline.my.fctriPad 1:biddable
## 1.53085043
## prdline.my.fctriPad 2:biddable
## 1.23456311
## prdline.my.fctriPad 3+:biddable
## -0.33006392
## prdline.my.fctriPadAir:biddable
## 0.67716427
## prdline.my.fctriPadmini:biddable
## 0.26579309
## prdline.my.fctriPadmini 2+:biddable
## -0.49404830
## prdline.my.fctriPad 1:condition.fctrFor parts or not working
## 1.31505108
## prdline.my.fctriPad 2:condition.fctrFor parts or not working
## 0.04720898
## prdline.my.fctriPad 3+:condition.fctrFor parts or not working
## -1.10049778
## prdline.my.fctriPadAir:condition.fctrFor parts or not working
## -0.65164355
## prdline.my.fctriPadmini:condition.fctrFor parts or not working
## -2.04524195
## prdline.my.fctriPadmini 2+:condition.fctrFor parts or not working
## -6.49351983
## prdline.my.fctriPad 3+:condition.fctrManufacturer refurbished
## -0.39362636
## prdline.my.fctriPadAir:condition.fctrManufacturer refurbished
## 1.65721588
## prdline.my.fctriPadmini:condition.fctrManufacturer refurbished
## -0.71681748
## prdline.my.fctriPadmini 2+:condition.fctrManufacturer refurbished
## -5.43641398
## prdline.my.fctriPad 1:condition.fctrNew
## -7.20520477
## prdline.my.fctriPad 3+:condition.fctrNew
## -0.69792998
## prdline.my.fctriPadAir:condition.fctrNew
## 0.09328170
## prdline.my.fctriPadmini:condition.fctrNew
## 0.55232416
## prdline.my.fctriPadmini 2+:condition.fctrNew
## 0.66313960
## prdline.my.fctriPad 1:condition.fctrNew other (see details)
## -3.51737875
## prdline.my.fctriPad 2:condition.fctrNew other (see details)
## -7.36641521
## prdline.my.fctriPad 3+:condition.fctrNew other (see details)
## -2.08783863
## prdline.my.fctriPadAir:condition.fctrNew other (see details)
## -1.47077618
## prdline.my.fctriPadmini:condition.fctrNew other (see details)
## -4.16372137
## prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)
## -4.98085945
## prdline.my.fctriPad 1:condition.fctrSeller refurbished
## 1.94195941
## prdline.my.fctriPad 2:condition.fctrSeller refurbished
## -0.64355020
## prdline.my.fctriPad 3+:condition.fctrSeller refurbished
## -1.35985695
## prdline.my.fctriPadAir:condition.fctrSeller refurbished
## -0.26033958
## prdline.my.fctriPadmini:condition.fctrSeller refurbished
## -2.04937581
## prdline.my.fctriPadmini 2+:condition.fctrSeller refurbished
## -7.81696445
## prdline.my.fctriPad 1:D.terms.n.post.stop
## 0.11340381
## prdline.my.fctriPad 2:D.terms.n.post.stop
## -0.14075275
## prdline.my.fctriPad 3+:D.terms.n.post.stop
## -0.20061215
## prdline.my.fctriPadAir:D.terms.n.post.stop
## -0.14406935
## prdline.my.fctriPadmini:D.terms.n.post.stop
## -0.08016961
## prdline.my.fctriPadmini 2+:D.terms.n.post.stop
## -0.15298258
## prdline.my.fctriPad 1:cellular.fctr1
## 1.85407557
## prdline.my.fctriPad 2:cellular.fctr1
## 1.48191584
## prdline.my.fctriPad 3+:cellular.fctr1
## 1.92386040
## prdline.my.fctriPadAir:cellular.fctr1
## 1.84032411
## prdline.my.fctriPadmini:cellular.fctr1
## 2.35197829
## prdline.my.fctriPadmini 2+:cellular.fctr1
## 2.45019432
## prdline.my.fctriPad 1:cellular.fctrUnknown
## -0.39042088
## prdline.my.fctriPad 2:cellular.fctrUnknown
## -1.03539067
## prdline.my.fctriPad 3+:cellular.fctrUnknown
## -0.48259476
## prdline.my.fctriPadAir:cellular.fctrUnknown
## 0.46061587
## prdline.my.fctriPadmini:cellular.fctrUnknown
## 0.40735463
## prdline.my.fctriPadmini 2+:cellular.fctrUnknown
## 0.12416427
## character(0)
## character(0)
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.63202247
## 2 0.1 0.63202247
## 3 0.2 0.63202247
## 4 0.3 0.76619100
## 5 0.4 0.76158940
## 6 0.5 0.75555556
## 7 0.6 0.75555556
## 8 0.7 0.40747029
## 9 0.8 0.01762115
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.glmnet.N sold.fctr.predict.csm.glmnet.Y
## 1 N 412 112
## 2 Y 101 349
## Prediction
## Reference N Y
## N 412 112
## Y 101 349
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.813142e-01 5.608471e-01 7.540110e-01 8.069046e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 3.367724e-56 4.932248e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.63320463
## 2 0.1 0.63320463
## 3 0.2 0.63320463
## 4 0.3 0.75434243
## 5 0.4 0.75561097
## 6 0.5 0.75282309
## 7 0.6 0.75282309
## 8 0.7 0.34404537
## 9 0.8 0.03333333
## 10 0.9 0.00000000
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.glmnet.N sold.fctr.predict.csm.glmnet.Y
## 1 N 386 89
## 2 Y 107 303
## Prediction
## Reference N Y
## N 386 89
## Y 107 303
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.785311e-01 5.533181e-01 7.497051e-01 8.054889e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 1.636469e-50 2.246386e-01
## model_id model_method
## 1 csm.glmnet glmnet
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 9 2.726 0.334
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8162405 0.3 0.766191 0.7741374
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.754011 0.8069046 0.5456204 0.781638
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.755611 0.7785311
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7497051 0.8054889 0.5533181
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008498508 0.01784782
## importance
## biddable 100.00000
## prdline.my.fctriPad 1:condition.fctrFor parts or not working 44.76341
## prdline.my.fctriPad 2:.clusterid.fctr4 32.34231
## prdline.my.fctriPad 1:biddable 24.60104
## prdline.my.fctriPad 2:biddable 23.41508
## prdline.my.fctriPad 1:condition.fctrSeller refurbished 10.87551
## [1] "fitting model: csm.rpart"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00333 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 974
##
## CP nsplit rel error
## 1 0.511111111 0 1.0000000
## 2 0.003333333 1 0.4888889
##
## Variable importance
## biddable prdline.my.fctriPad 3+:biddable
## 55 10
## prdline.my.fctriPadAir:biddable prdline.my.fctriPad 1:biddable
## 9 9
## prdline.my.fctriPadmini:biddable prdline.my.fctriPad 2:biddable
## 9 8
##
## Node number 1: 974 observations, complexity param=0.5111111
## predicted class=N expected loss=0.4620123 P(node) =1
## class counts: 524 450
## probabilities: 0.538 0.462
## left son=2 (524 obs) right son=3 (450 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=144.14990, (0 missing)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, improve= 25.37925, (0 missing)
## prdline.my.fctriPad 2:biddable < 0.5 to the left, improve= 22.83949, (0 missing)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, improve= 16.00553, (0 missing)
## prdline.my.fctriPad 3+:biddable < 0.5 to the left, improve= 14.01384, (0 missing)
## Surrogate splits:
## prdline.my.fctriPad 3+:biddable < 0.5 to the left, agree=0.624, adj=0.187, (0 split)
## prdline.my.fctriPadAir:biddable < 0.5 to the left, agree=0.618, adj=0.173, (0 split)
## prdline.my.fctriPad 1:biddable < 0.5 to the left, agree=0.613, adj=0.162, (0 split)
## prdline.my.fctriPadmini:biddable < 0.5 to the left, agree=0.611, adj=0.158, (0 split)
## prdline.my.fctriPad 2:biddable < 0.5 to the left, agree=0.606, adj=0.147, (0 split)
##
## Node number 2: 524 observations
## predicted class=N expected loss=0.2099237 P(node) =0.5379877
## class counts: 414 110
## probabilities: 0.790 0.210
##
## Node number 3: 450 observations
## predicted class=Y expected loss=0.2444444 P(node) =0.4620123
## class counts: 110 340
## probabilities: 0.244 0.756
##
## n= 974
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 974 450 N (0.5379877 0.4620123)
## 2) biddable< 0.5 524 110 N (0.7900763 0.2099237) *
## 3) biddable>=0.5 450 110 Y (0.2444444 0.7555556) *
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.6320225
## 3 0.2 0.6320225
## 4 0.3 0.7555556
## 5 0.4 0.7555556
## 6 0.5 0.7555556
## 7 0.6 0.7555556
## 8 0.7 0.7555556
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.rpart.N sold.fctr.predict.csm.rpart.Y
## 1 N 414 110
## 2 Y 110 340
## Prediction
## Reference N Y
## N 414 110
## Y 110 340
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.741273e-01 5.456319e-01 7.465456e-01 8.000405e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 7.516456e-53 1.000000e+00
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6332046
## 3 0.2 0.6332046
## 4 0.3 0.7528231
## 5 0.4 0.7528231
## 6 0.5 0.7528231
## 7 0.6 0.7528231
## 8 0.7 0.7528231
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.rpart.N sold.fctr.predict.csm.rpart.Y
## 1 N 388 87
## 2 Y 110 300
## Prediction
## Reference N Y
## N 388 87
## Y 110 300
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.774011e-01 5.506630e-01 7.485294e-01 8.044124e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.956102e-50 1.170130e-01
## model_id model_method
## 1 csm.rpart rpart
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.698 0.075
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7728159 0.7 0.7555556 0.7669389
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7465456 0.8000405 0.5300413 0.7742747
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.7528231 0.7774011
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7485294 0.8044124 0.550663
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.002017353 0.007393625
## importance
## biddable 100.000000
## prdline.my.fctriPad 1:biddable 17.606151
## prdline.my.fctriPad 2:biddable 15.844266
## prdline.my.fctriPadAir:biddable 11.103395
## prdline.my.fctriPad 3+:biddable 9.721717
## `prdline.my.fctriPad 1` 0.000000
## [1] "fitting model: csm.rf"
## [1] " indep_vars: prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 53 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 104 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 104 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6320225
## 2 0.1 0.7834275
## 3 0.2 0.8244734
## 4 0.3 0.8550420
## 5 0.4 0.8758170
## 6 0.5 0.8757127
## 7 0.6 0.8667439
## 8 0.7 0.8256659
## 9 0.8 0.7779204
## 10 0.9 0.6961326
## 11 1.0 0.3824561
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.csm.rf.N sold.fctr.predict.csm.rf.Y
## 1 N 458 66
## 2 Y 48 402
## Prediction
## Reference N Y
## N 458 66
## Y 48 402
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.829569e-01 7.652178e-01 8.610882e-01 9.024774e-01 5.379877e-01
## AccuracyPValue McnemarPValue
## 4.046280e-119 1.113407e-01
## [1] " calling mypredict_mdl for OOB:"
## threshold f.score
## 1 0.0 0.6332046
## 2 0.1 0.6957404
## 3 0.2 0.7063922
## 4 0.3 0.7129630
## 5 0.4 0.7024390
## 6 0.5 0.6991037
## 7 0.6 0.6905710
## 8 0.7 0.6583679
## 9 0.8 0.6171761
## 10 0.9 0.5651491
## 11 1.0 0.2510288
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## sold.fctr sold.fctr.predict.csm.rf.N sold.fctr.predict.csm.rf.Y
## 1 N 329 146
## 2 Y 102 308
## Prediction
## Reference N Y
## N 329 146
## Y 102 308
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.197740e-01 4.406158e-01 6.889217e-01 7.491552e-01 5.367232e-01
## AccuracyPValue McnemarPValue
## 4.973065e-29 6.323781e-03
## model_id model_method
## 1 csm.rf rf
## feats
## 1 prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 22.131 7.953
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.907888 0.4 0.875817 0.728968
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8610882 0.9024774 0.4528104 0.7665417
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.712963 0.719774
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.6889217 0.7491552 0.4406158
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0167801 0.0311865
## importance
## biddable 100.000000
## D.terms.n.post.stop 22.608839
## prdline.my.fctriPad 3+:D.terms.n.post.stop 8.017530
## cellular.fctrUnknown 6.252631
## prdline.my.fctriPad 1:biddable 5.977706
## cellular.fctr1 5.750988###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Max.cor.Y.glm Max.cor.Y.glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm
## Low.cor.X.glm Low.cor.X.glm
## All.X.glm All.X.glm
## All.X.bayesglm All.X.bayesglm
## All.X.glmnet All.X.glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf
## All.Interact.X.glm All.Interact.X.glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf
## csm.glm csm.glm
## csm.bayesglm csm.bayesglm
## csm.glmnet csm.glmnet
## csm.rpart csm.rpart
## csm.rf csm.rf
## model_method
## MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart rpart
## Max.cor.Y.glm glm
## Interact.High.cor.Y.glm glm
## Low.cor.X.glm glm
## All.X.glm glm
## All.X.bayesglm bayesglm
## All.X.glmnet glmnet
## All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf rf
## All.Interact.X.glm glm
## All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf rf
## csm.glm glm
## csm.bayesglm bayesglm
## csm.glmnet glmnet
## csm.rpart rpart
## csm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.diff
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.diff
## Max.cor.Y.rpart biddable, startprice.diff
## Max.cor.Y.glm biddable, startprice.diff
## Interact.High.cor.Y.glm biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.npnct24.log, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:D.terms.n.post.stop.log, biddable:cellular.fctr, biddable:D.nwrds.unq.log
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, color.fctr, D.npnct08.log, prdline.my.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.438
## Random.myrandom_classfr 0 0.253
## Max.cor.Y.cv.0.rpart 0 0.610
## Max.cor.Y.cv.0.cp.0.rpart 0 0.488
## Max.cor.Y.rpart 3 1.072
## Max.cor.Y.glm 1 1.004
## Interact.High.cor.Y.glm 1 1.047
## Low.cor.X.glm 1 1.490
## All.X.glm 1 1.801
## All.X.bayesglm 1 3.602
## All.X.glmnet 9 5.826
## All.X.no.rnorm.rpart 3 1.673
## All.X.no.rnorm.rf 3 16.547
## All.Interact.X.glm 1 1.972
## All.Interact.X.bayesglm 1 2.669
## All.Interact.X.glmnet 9 9.088
## All.Interact.X.no.rnorm.rpart 3 1.854
## All.Interact.X.no.rnorm.rf 3 20.893
## csm.glm 1 1.892
## csm.bayesglm 1 2.058
## csm.glmnet 9 2.726
## csm.rpart 3 1.698
## csm.rf 3 22.131
## min.elapsedtime.final max.auc.fit
## MFO.myMFO_classfr 0.002 0.5000000
## Random.myrandom_classfr 0.001 0.5071756
## Max.cor.Y.cv.0.rpart 0.012 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.009 0.8855386
## Max.cor.Y.rpart 0.012 0.8243427
## Max.cor.Y.glm 0.011 0.8586302
## Interact.High.cor.Y.glm 0.019 0.8580831
## Low.cor.X.glm 0.182 0.8858185
## All.X.glm 0.258 0.8936980
## All.X.bayesglm 0.371 0.8912129
## All.X.glmnet 1.167 0.8592409
## All.X.no.rnorm.rpart 0.069 0.8243427
## All.X.no.rnorm.rf 5.082 1.0000000
## All.Interact.X.glm 0.471 0.9076718
## All.Interact.X.bayesglm 0.644 0.9015225
## All.Interact.X.glmnet 0.736 0.8845377
## All.Interact.X.no.rnorm.rpart 0.093 0.8243427
## All.Interact.X.no.rnorm.rf 7.009 1.0000000
## csm.glm 0.434 0.8645971
## csm.bayesglm 0.424 0.8626760
## csm.glmnet 0.334 0.8162405
## csm.rpart 0.075 0.7728159
## csm.rf 7.953 0.9078880
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.5 0.8113208
## Max.cor.Y.rpart 0.9 0.7633987
## Max.cor.Y.glm 0.7 0.7639594
## Interact.High.cor.Y.glm 0.7 0.7615385
## Low.cor.X.glm 0.5 0.7977143
## All.X.glm 0.5 0.8132875
## All.X.bayesglm 0.5 0.8127128
## All.X.glmnet 0.6 0.7666667
## All.X.no.rnorm.rpart 0.9 0.7633987
## All.X.no.rnorm.rf 0.6 1.0000000
## All.Interact.X.glm 0.4 0.8219485
## All.Interact.X.bayesglm 0.6 0.8186275
## All.Interact.X.glmnet 0.6 0.7965044
## All.Interact.X.no.rnorm.rpart 0.9 0.7633987
## All.Interact.X.no.rnorm.rf 0.5 1.0000000
## csm.glm 0.3 0.7770961
## csm.bayesglm 0.4 0.7789934
## csm.glmnet 0.3 0.7661910
## csm.rpart 0.7 0.7555556
## csm.rf 0.4 0.8758170
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.5379877 0.5060896
## Random.myrandom_classfr 0.4620123 0.4303445
## Max.cor.Y.cv.0.rpart 0.5379877 0.5060896
## Max.cor.Y.cv.0.cp.0.rpart 0.8357290 0.8109392
## Max.cor.Y.rpart 0.7833523 0.7882906
## Max.cor.Y.glm 0.7720798 0.7829169
## Interact.High.cor.Y.glm 0.7741374 0.7829169
## Low.cor.X.glm 0.7710320 0.7925945
## All.X.glm 0.7679550 0.8076954
## All.X.bayesglm 0.7638683 0.8055345
## All.X.glmnet 0.7864482 0.7721896
## All.X.no.rnorm.rpart 0.8018645 0.7882906
## All.X.no.rnorm.rf 0.8018392 0.9962198
## All.Interact.X.glm 0.7494967 0.8120210
## All.Interact.X.bayesglm 0.7813200 0.8239439
## All.Interact.X.glmnet 0.7977651 0.8076954
## All.Interact.X.no.rnorm.rpart 0.8018645 0.7882906
## All.Interact.X.no.rnorm.rf 0.7823457 0.9962198
## csm.glm 0.7310098 0.7486774
## csm.bayesglm 0.7576923 0.7657655
## csm.glmnet 0.7741374 0.7540110
## csm.rpart 0.7669389 0.7465456
## csm.rf 0.7289680 0.8610882
## max.AccuracyUpper.fit max.Kappa.fit
## MFO.myMFO_classfr 0.5696555 0.0000000
## Random.myrandom_classfr 0.4939104 0.0000000
## Max.cor.Y.cv.0.rpart 0.5696555 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8584688 0.6668320
## Max.cor.Y.rpart 0.8381305 0.5585199
## Max.cor.Y.glm 0.8332690 0.5402037
## Interact.High.cor.Y.glm 0.8332690 0.5436041
## Low.cor.X.glm 0.8420145 0.5386776
## All.X.glm 0.8555717 0.5327262
## All.X.bayesglm 0.8536386 0.5243770
## All.X.glmnet 0.8235260 0.5692702
## All.X.no.rnorm.rpart 0.8381305 0.5940341
## All.X.no.rnorm.rf 1.0000000 0.5970410
## All.Interact.X.glm 0.8594339 0.4949703
## All.Interact.X.bayesglm 0.8700277 0.5584387
## All.Interact.X.glmnet 0.8555717 0.5903552
## All.Interact.X.no.rnorm.rpart 0.8381305 0.5940341
## All.Interact.X.no.rnorm.rf 1.0000000 0.5584003
## csm.glm 0.8020028 0.4584371
## csm.bayesglm 0.8176677 0.5127898
## csm.glmnet 0.8069046 0.5456204
## csm.rpart 0.8000405 0.5300413
## csm.rf 0.9024774 0.4528104
## max.auc.OOB opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.5000000 0.5
## Random.myrandom_classfr 0.5191913 0.4
## Max.cor.Y.cv.0.rpart 0.5000000 0.5
## Max.cor.Y.cv.0.cp.0.rpart 0.8187625 0.5
## Max.cor.Y.rpart 0.8129705 0.3
## Max.cor.Y.glm 0.8633582 0.6
## Interact.High.cor.Y.glm 0.8615815 0.6
## Low.cor.X.glm 0.8465571 0.5
## All.X.glm 0.8431528 0.5
## All.X.bayesglm 0.8462850 0.5
## All.X.glmnet 0.8631938 0.6
## All.X.no.rnorm.rpart 0.8129705 0.3
## All.X.no.rnorm.rf 0.8637792 0.5
## All.Interact.X.glm 0.8344904 0.5
## All.Interact.X.bayesglm 0.8364519 0.6
## All.Interact.X.glmnet 0.8510347 0.5
## All.Interact.X.no.rnorm.rpart 0.8129705 0.3
## All.Interact.X.no.rnorm.rf 0.8559487 0.6
## csm.glm 0.7577818 0.4
## csm.bayesglm 0.7749705 0.4
## csm.glmnet 0.7816380 0.4
## csm.rpart 0.7742747 0.7
## csm.rf 0.7665417 0.3
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.0000000 0.5367232
## Random.myrandom_classfr 0.6332046 0.4632768
## Max.cor.Y.cv.0.rpart 0.0000000 0.5367232
## Max.cor.Y.cv.0.cp.0.rpart 0.7551546 0.7853107
## Max.cor.Y.rpart 0.7528231 0.7774011
## Max.cor.Y.glm 0.7710526 0.8033898
## Interact.High.cor.Y.glm 0.7665782 0.8011299
## Low.cor.X.glm 0.7733675 0.8000000
## All.X.glm 0.7715736 0.7966102
## All.X.bayesglm 0.7708067 0.7977401
## All.X.glmnet 0.7751323 0.8079096
## All.X.no.rnorm.rpart 0.7528231 0.7774011
## All.X.no.rnorm.rf 0.7854356 0.8135593
## All.Interact.X.glm 0.7573813 0.7864407
## All.Interact.X.bayesglm 0.7573333 0.7943503
## All.Interact.X.glmnet 0.7630208 0.7943503
## All.Interact.X.no.rnorm.rpart 0.7528231 0.7774011
## All.Interact.X.no.rnorm.rf 0.7747253 0.8146893
## csm.glm 0.7148014 0.7322034
## csm.bayesglm 0.7310513 0.7514124
## csm.glmnet 0.7556110 0.7785311
## csm.rpart 0.7528231 0.7774011
## csm.rf 0.7129630 0.7197740
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO.myMFO_classfr 0.5032294 0.5699717
## Random.myrandom_classfr 0.4300283 0.4967706
## Max.cor.Y.cv.0.rpart 0.5032294 0.5699717
## Max.cor.Y.cv.0.cp.0.rpart 0.7567658 0.8119416
## Max.cor.Y.rpart 0.7485294 0.8044124
## Max.cor.Y.glm 0.7756483 0.8290941
## Interact.High.cor.Y.glm 0.7732835 0.8269546
## Low.cor.X.glm 0.7721016 0.8258843
## All.X.glm 0.7685578 0.8226715
## All.X.bayesglm 0.7697388 0.8237428
## All.X.glmnet 0.7803820 0.8333692
## All.X.no.rnorm.rpart 0.7485294 0.8044124
## All.X.no.rnorm.rf 0.7863067 0.8387053
## All.Interact.X.glm 0.7579436 0.8130160
## All.Interact.X.bayesglm 0.7661969 0.8205280
## All.Interact.X.glmnet 0.7661969 0.8205280
## All.Interact.X.no.rnorm.rpart 0.7485294 0.8044124
## All.Interact.X.no.rnorm.rf 0.7874927 0.8397715
## csm.glm 0.7017229 0.7611290
## csm.bayesglm 0.7215645 0.7795759
## csm.glmnet 0.7497051 0.8054889
## csm.rpart 0.7485294 0.8044124
## csm.rf 0.6889217 0.7491552
## max.Kappa.OOB max.AccuracySD.fit
## MFO.myMFO_classfr 0.0000000 NA
## Random.myrandom_classfr 0.0000000 NA
## Max.cor.Y.cv.0.rpart 0.0000000 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.5650993 NA
## Max.cor.Y.rpart 0.5506630 0.017790747
## Max.cor.Y.glm 0.6006483 0.005815317
## Interact.High.cor.Y.glm 0.5956491 0.008498508
## Low.cor.X.glm 0.5951959 0.015836744
## All.X.glm 0.5888183 0.012778378
## All.X.bayesglm 0.5906219 0.007372855
## All.X.glmnet 0.6095656 0.004643467
## All.X.no.rnorm.rpart 0.5506630 0.013888207
## All.X.no.rnorm.rf 0.6218781 0.014398891
## All.Interact.X.glm 0.5676063 0.012517513
## All.Interact.X.bayesglm 0.5815820 0.004951066
## All.Interact.X.glmnet 0.5828499 0.020634110
## All.Interact.X.no.rnorm.rpart 0.5506630 0.013888207
## All.Interact.X.no.rnorm.rf 0.6215582 0.006148854
## csm.glm 0.4624886 0.018716928
## csm.bayesglm 0.4999615 0.008104349
## csm.glmnet 0.5533181 0.008498508
## csm.rpart 0.5506630 0.002017353
## csm.rf 0.4406158 0.016780098
## max.KappaSD.fit min.aic.fit
## MFO.myMFO_classfr NA NA
## Random.myrandom_classfr NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.033543790 NA
## Max.cor.Y.glm 0.011249498 919.4841
## Interact.High.cor.Y.glm 0.018207150 931.8178
## Low.cor.X.glm 0.032711928 938.3505
## All.X.glm 0.026469200 948.3929
## All.X.bayesglm 0.014708213 992.7758
## All.X.glmnet 0.012261960 NA
## All.X.no.rnorm.rpart 0.029572029 NA
## All.X.no.rnorm.rf 0.028547614 NA
## All.Interact.X.glm 0.028576855 942.9274
## All.Interact.X.bayesglm 0.012171076 1023.1966
## All.Interact.X.glmnet 0.042680167 NA
## All.Interact.X.no.rnorm.rpart 0.029572029 NA
## All.Interact.X.no.rnorm.rf 0.013203334 NA
## csm.glm 0.043145565 1061.9948
## csm.bayesglm 0.018727383 1106.2071
## csm.glmnet 0.017847823 NA
## csm.rpart 0.007393625 NA
## csm.rf 0.031186496 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 11 fit.models_1_rf 11 0 256.413 327.355 70.942
## 12 fit.models_1_end 12 0 327.356 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 161.264 327.363 166.099
## 12 fit.models 7 2 327.364 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Max.cor.Y.glm Max.cor.Y.glm
## Interact.High.cor.Y.glm Interact.High.cor.Y.glm
## Low.cor.X.glm Low.cor.X.glm
## All.X.glm All.X.glm
## All.X.bayesglm All.X.bayesglm
## All.X.glmnet All.X.glmnet
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf
## All.Interact.X.glm All.Interact.X.glm
## All.Interact.X.bayesglm All.Interact.X.bayesglm
## All.Interact.X.glmnet All.Interact.X.glmnet
## All.Interact.X.no.rnorm.rpart All.Interact.X.no.rnorm.rpart
## All.Interact.X.no.rnorm.rf All.Interact.X.no.rnorm.rf
## csm.glm csm.glm
## csm.bayesglm csm.bayesglm
## csm.glmnet csm.glmnet
## csm.rpart csm.rpart
## csm.rf csm.rf
## model_method
## MFO.myMFO_classfr myMFO_classfr
## Random.myrandom_classfr myrandom_classfr
## Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart rpart
## Max.cor.Y.glm glm
## Interact.High.cor.Y.glm glm
## Low.cor.X.glm glm
## All.X.glm glm
## All.X.bayesglm bayesglm
## All.X.glmnet glmnet
## All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf rf
## All.Interact.X.glm glm
## All.Interact.X.bayesglm bayesglm
## All.Interact.X.glmnet glmnet
## All.Interact.X.no.rnorm.rpart rpart
## All.Interact.X.no.rnorm.rf rf
## csm.glm glm
## csm.bayesglm bayesglm
## csm.glmnet glmnet
## csm.rpart rpart
## csm.rf rf
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.cv.0.rpart biddable, startprice.diff
## Max.cor.Y.cv.0.cp.0.rpart biddable, startprice.diff
## Max.cor.Y.rpart biddable, startprice.diff
## Max.cor.Y.glm biddable, startprice.diff
## Interact.High.cor.Y.glm biddable, startprice.diff, biddable:D.terms.n.post.stop, biddable:D.TfIdf.sum.post.stem, biddable:D.npnct24.log, biddable:D.npnct06.log, biddable:D.ratio.nstopwrds.nwrds, biddable:D.nchrs.log, biddable:D.nwrds.log, biddable:D.terms.n.post.stop.log, biddable:cellular.fctr, biddable:D.nwrds.unq.log
## Low.cor.X.glm biddable, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, color.fctr, D.npnct08.log, prdline.my.fctr, D.npnct06.log, D.npnct28.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.bayesglm biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.glmnet biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, .rnorm, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rpart biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.X.no.rnorm.rf biddable, D.ratio.nstopwrds.nwrds, D.npnct15.log, D.npnct03.log, D.terms.n.stem.stop.Ratio, D.ratio.sum.TfIdf.nwrds, D.npnct01.log, D.TfIdf.sum.stem.stop.Ratio, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, carrier.fctr, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, cellular.fctr, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, idseq.my, startprice.diff, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glm D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.bayesglm D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.glmnet D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, .rnorm, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rpart D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## All.Interact.X.no.rnorm.rf D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## csm.glm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.bayesglm prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.glmnet prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rpart prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## csm.rf prdline.my.fctr, prdline.my.fctr:.clusterid.fctr, prdline.my.fctr*biddable, prdline.my.fctr*condition.fctr, prdline.my.fctr*D.terms.n.post.stop, prdline.my.fctr*cellular.fctr
## max.nTuningRuns max.auc.fit
## MFO.myMFO_classfr 0 0.5000000
## Random.myrandom_classfr 0 0.5071756
## Max.cor.Y.cv.0.rpart 0 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.8855386
## Max.cor.Y.rpart 3 0.8243427
## Max.cor.Y.glm 1 0.8586302
## Interact.High.cor.Y.glm 1 0.8580831
## Low.cor.X.glm 1 0.8858185
## All.X.glm 1 0.8936980
## All.X.bayesglm 1 0.8912129
## All.X.glmnet 9 0.8592409
## All.X.no.rnorm.rpart 3 0.8243427
## All.X.no.rnorm.rf 3 1.0000000
## All.Interact.X.glm 1 0.9076718
## All.Interact.X.bayesglm 1 0.9015225
## All.Interact.X.glmnet 9 0.8845377
## All.Interact.X.no.rnorm.rpart 3 0.8243427
## All.Interact.X.no.rnorm.rf 3 1.0000000
## csm.glm 1 0.8645971
## csm.bayesglm 1 0.8626760
## csm.glmnet 9 0.8162405
## csm.rpart 3 0.7728159
## csm.rf 3 0.9078880
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6320225
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.5 0.8113208
## Max.cor.Y.rpart 0.9 0.7633987
## Max.cor.Y.glm 0.7 0.7639594
## Interact.High.cor.Y.glm 0.7 0.7615385
## Low.cor.X.glm 0.5 0.7977143
## All.X.glm 0.5 0.8132875
## All.X.bayesglm 0.5 0.8127128
## All.X.glmnet 0.6 0.7666667
## All.X.no.rnorm.rpart 0.9 0.7633987
## All.X.no.rnorm.rf 0.6 1.0000000
## All.Interact.X.glm 0.4 0.8219485
## All.Interact.X.bayesglm 0.6 0.8186275
## All.Interact.X.glmnet 0.6 0.7965044
## All.Interact.X.no.rnorm.rpart 0.9 0.7633987
## All.Interact.X.no.rnorm.rf 0.5 1.0000000
## csm.glm 0.3 0.7770961
## csm.bayesglm 0.4 0.7789934
## csm.glmnet 0.3 0.7661910
## csm.rpart 0.7 0.7555556
## csm.rf 0.4 0.8758170
## max.Accuracy.fit max.Kappa.fit max.auc.OOB
## MFO.myMFO_classfr 0.5379877 0.0000000 0.5000000
## Random.myrandom_classfr 0.4620123 0.0000000 0.5191913
## Max.cor.Y.cv.0.rpart 0.5379877 0.0000000 0.5000000
## Max.cor.Y.cv.0.cp.0.rpart 0.8357290 0.6668320 0.8187625
## Max.cor.Y.rpart 0.7833523 0.5585199 0.8129705
## Max.cor.Y.glm 0.7720798 0.5402037 0.8633582
## Interact.High.cor.Y.glm 0.7741374 0.5436041 0.8615815
## Low.cor.X.glm 0.7710320 0.5386776 0.8465571
## All.X.glm 0.7679550 0.5327262 0.8431528
## All.X.bayesglm 0.7638683 0.5243770 0.8462850
## All.X.glmnet 0.7864482 0.5692702 0.8631938
## All.X.no.rnorm.rpart 0.8018645 0.5940341 0.8129705
## All.X.no.rnorm.rf 0.8018392 0.5970410 0.8637792
## All.Interact.X.glm 0.7494967 0.4949703 0.8344904
## All.Interact.X.bayesglm 0.7813200 0.5584387 0.8364519
## All.Interact.X.glmnet 0.7977651 0.5903552 0.8510347
## All.Interact.X.no.rnorm.rpart 0.8018645 0.5940341 0.8129705
## All.Interact.X.no.rnorm.rf 0.7823457 0.5584003 0.8559487
## csm.glm 0.7310098 0.4584371 0.7577818
## csm.bayesglm 0.7576923 0.5127898 0.7749705
## csm.glmnet 0.7741374 0.5456204 0.7816380
## csm.rpart 0.7669389 0.5300413 0.7742747
## csm.rf 0.7289680 0.4528104 0.7665417
## opt.prob.threshold.OOB max.f.score.OOB
## MFO.myMFO_classfr 0.5 0.0000000
## Random.myrandom_classfr 0.4 0.6332046
## Max.cor.Y.cv.0.rpart 0.5 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.5 0.7551546
## Max.cor.Y.rpart 0.3 0.7528231
## Max.cor.Y.glm 0.6 0.7710526
## Interact.High.cor.Y.glm 0.6 0.7665782
## Low.cor.X.glm 0.5 0.7733675
## All.X.glm 0.5 0.7715736
## All.X.bayesglm 0.5 0.7708067
## All.X.glmnet 0.6 0.7751323
## All.X.no.rnorm.rpart 0.3 0.7528231
## All.X.no.rnorm.rf 0.5 0.7854356
## All.Interact.X.glm 0.5 0.7573813
## All.Interact.X.bayesglm 0.6 0.7573333
## All.Interact.X.glmnet 0.5 0.7630208
## All.Interact.X.no.rnorm.rpart 0.3 0.7528231
## All.Interact.X.no.rnorm.rf 0.6 0.7747253
## csm.glm 0.4 0.7148014
## csm.bayesglm 0.4 0.7310513
## csm.glmnet 0.4 0.7556110
## csm.rpart 0.7 0.7528231
## csm.rf 0.3 0.7129630
## max.Accuracy.OOB max.Kappa.OOB
## MFO.myMFO_classfr 0.5367232 0.0000000
## Random.myrandom_classfr 0.4632768 0.0000000
## Max.cor.Y.cv.0.rpart 0.5367232 0.0000000
## Max.cor.Y.cv.0.cp.0.rpart 0.7853107 0.5650993
## Max.cor.Y.rpart 0.7774011 0.5506630
## Max.cor.Y.glm 0.8033898 0.6006483
## Interact.High.cor.Y.glm 0.8011299 0.5956491
## Low.cor.X.glm 0.8000000 0.5951959
## All.X.glm 0.7966102 0.5888183
## All.X.bayesglm 0.7977401 0.5906219
## All.X.glmnet 0.8079096 0.6095656
## All.X.no.rnorm.rpart 0.7774011 0.5506630
## All.X.no.rnorm.rf 0.8135593 0.6218781
## All.Interact.X.glm 0.7864407 0.5676063
## All.Interact.X.bayesglm 0.7943503 0.5815820
## All.Interact.X.glmnet 0.7943503 0.5828499
## All.Interact.X.no.rnorm.rpart 0.7774011 0.5506630
## All.Interact.X.no.rnorm.rf 0.8146893 0.6215582
## csm.glm 0.7322034 0.4624886
## csm.bayesglm 0.7514124 0.4999615
## csm.glmnet 0.7785311 0.5533181
## csm.rpart 0.7774011 0.5506630
## csm.rf 0.7197740 0.4406158
## inv.elapsedtime.everything
## MFO.myMFO_classfr 2.28310502
## Random.myrandom_classfr 3.95256917
## Max.cor.Y.cv.0.rpart 1.63934426
## Max.cor.Y.cv.0.cp.0.rpart 2.04918033
## Max.cor.Y.rpart 0.93283582
## Max.cor.Y.glm 0.99601594
## Interact.High.cor.Y.glm 0.95510984
## Low.cor.X.glm 0.67114094
## All.X.glm 0.55524708
## All.X.bayesglm 0.27762354
## All.X.glmnet 0.17164435
## All.X.no.rnorm.rpart 0.59772863
## All.X.no.rnorm.rf 0.06043392
## All.Interact.X.glm 0.50709939
## All.Interact.X.bayesglm 0.37467216
## All.Interact.X.glmnet 0.11003521
## All.Interact.X.no.rnorm.rpart 0.53937433
## All.Interact.X.no.rnorm.rf 0.04786292
## csm.glm 0.52854123
## csm.bayesglm 0.48590865
## csm.glmnet 0.36683786
## csm.rpart 0.58892815
## csm.rf 0.04518549
## inv.elapsedtime.final inv.aic.fit
## MFO.myMFO_classfr 500.0000000 NA
## Random.myrandom_classfr 1000.0000000 NA
## Max.cor.Y.cv.0.rpart 83.3333333 NA
## Max.cor.Y.cv.0.cp.0.rpart 111.1111111 NA
## Max.cor.Y.rpart 83.3333333 NA
## Max.cor.Y.glm 90.9090909 0.0010875664
## Interact.High.cor.Y.glm 52.6315789 0.0010731712
## Low.cor.X.glm 5.4945055 0.0010656999
## All.X.glm 3.8759690 0.0010544153
## All.X.bayesglm 2.6954178 0.0010072767
## All.X.glmnet 0.8568980 NA
## All.X.no.rnorm.rpart 14.4927536 NA
## All.X.no.rnorm.rf 0.1967729 NA
## All.Interact.X.glm 2.1231423 0.0010605270
## All.Interact.X.bayesglm 1.5527950 0.0009773293
## All.Interact.X.glmnet 1.3586957 NA
## All.Interact.X.no.rnorm.rpart 10.7526882 NA
## All.Interact.X.no.rnorm.rf 0.1426737 NA
## csm.glm 2.3041475 0.0009416242
## csm.bayesglm 2.3584906 0.0009039899
## csm.glmnet 2.9940120 NA
## csm.rpart 13.3333333 NA
## csm.rf 0.1257387 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.## Warning: Removed 5 rows containing missing values (geom_path).## Warning: Removed 247 rows containing missing values (geom_point).## Warning: Removed 14 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id max.Accuracy.OOB max.auc.OOB
## 18 All.Interact.X.no.rnorm.rf 0.8146893 0.8559487
## 13 All.X.no.rnorm.rf 0.8135593 0.8637792
## 11 All.X.glmnet 0.8079096 0.8631938
## 6 Max.cor.Y.glm 0.8033898 0.8633582
## 7 Interact.High.cor.Y.glm 0.8011299 0.8615815
## 8 Low.cor.X.glm 0.8000000 0.8465571
## 10 All.X.bayesglm 0.7977401 0.8462850
## 9 All.X.glm 0.7966102 0.8431528
## 16 All.Interact.X.glmnet 0.7943503 0.8510347
## 15 All.Interact.X.bayesglm 0.7943503 0.8364519
## 14 All.Interact.X.glm 0.7864407 0.8344904
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7853107 0.8187625
## 21 csm.glmnet 0.7785311 0.7816380
## 5 Max.cor.Y.rpart 0.7774011 0.8129705
## 12 All.X.no.rnorm.rpart 0.7774011 0.8129705
## 17 All.Interact.X.no.rnorm.rpart 0.7774011 0.8129705
## 22 csm.rpart 0.7774011 0.7742747
## 20 csm.bayesglm 0.7514124 0.7749705
## 19 csm.glm 0.7322034 0.7577818
## 23 csm.rf 0.7197740 0.7665417
## 1 MFO.myMFO_classfr 0.5367232 0.5000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913
## max.Kappa.OOB min.aic.fit opt.prob.threshold.OOB
## 18 0.6215582 NA 0.6
## 13 0.6218781 NA 0.5
## 11 0.6095656 NA 0.6
## 6 0.6006483 919.4841 0.6
## 7 0.5956491 931.8178 0.6
## 8 0.5951959 938.3505 0.5
## 10 0.5906219 992.7758 0.5
## 9 0.5888183 948.3929 0.5
## 16 0.5828499 NA 0.5
## 15 0.5815820 1023.1966 0.6
## 14 0.5676063 942.9274 0.5
## 4 0.5650993 NA 0.5
## 21 0.5533181 NA 0.4
## 5 0.5506630 NA 0.3
## 12 0.5506630 NA 0.3
## 17 0.5506630 NA 0.3
## 22 0.5506630 NA 0.7
## 20 0.4999615 1106.2071 0.4
## 19 0.4624886 1061.9948 0.4
## 23 0.4406158 NA 0.3
## 1 0.0000000 NA 0.5
## 3 0.0000000 NA 0.5
## 2 0.0000000 NA 0.4print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.## Warning: Removed 105 rows containing missing values (geom_point).## Warning: Removed 14 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 23. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.auc.OOB - max.Kappa.OOB + min.aic.fit -
## opt.prob.threshold.OOBprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.Interact.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
# if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
# warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
# glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
# }
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 974 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1948 matrix numeric
## oob.times 974 -none- numeric
## classes 2 -none- character
## importance 140 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 974 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 140 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
predct_error_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".err")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## biddable 1.000000e+02
## startprice.diff:biddable 7.295709e+01
## startprice.diff 6.287101e+01
## idseq.my 4.557866e+01
## prdline.my.fctriPadmini:idseq.my 8.119212e+00
## prdline.my.fctriPadAir:idseq.my 8.102069e+00
## D.ratio.sum.TfIdf.nwrds 6.787692e+00
## prdline.my.fctriPad 3+:idseq.my 6.463037e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.541186e+00
## prdline.my.fctriPad 1:idseq.my 5.448197e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 4.744887e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 4.653410e+00
## D.TfIdf.sum.stem.stop.Ratio 4.583426e+00
## storage.fctr64 4.562079e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 4.372017e+00
## color.fctrWhite 3.982307e+00
## D.ratio.nstopwrds.nwrds 3.747225e+00
## storage.fctrUnknown 3.656495e+00
## D.nstopwrds.log 3.501850e+00
## storage.fctr16 3.457889e+00
## D.sum.TfIdf 3.363877e+00
## color.fctrUnknown 3.345165e+00
## D.TfIdf.sum.post.stem 3.183039e+00
## cellular.fctr1 3.009368e+00
## D.TfIdf.sum.post.stop 2.954056e+00
## prdline.my.fctriPad 2:idseq.my 2.914295e+00
## condition.fctrNew 2.772069e+00
## D.nchrs.log 2.742885e+00
## D.nwrds.log 2.697258e+00
## D.nuppr.log 2.691101e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 2.637843e+00
## carrier.fctrNone 2.620120e+00
## carrier.fctrUnknown 2.136666e+00
## color.fctrSpace Gray 2.049710e+00
## condition.fctrNew other (see details) 1.824646e+00
## storage.fctr32 1.763924e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.756634e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 1.669334e+00
## D.terms.n.post.stem.log 1.590060e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 1.578635e+00
## D.terms.n.post.stem 1.567921e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.567397e+00
## D.nwrds.unq.log 1.539712e+00
## carrier.fctrVerizon 1.526833e+00
## cellular.fctr1:carrier.fctrVerizon 1.485664e+00
## D.npnct13.log 1.427327e+00
## color.fctrGold 1.423172e+00
## D.terms.n.post.stop.log 1.421139e+00
## cellular.fctrUnknown 1.374536e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.361053e+00
## prdline.my.fctriPadAir 1.359004e+00
## condition.fctrManufacturer refurbished 1.353844e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.334240e+00
## D.terms.n.stem.stop.Ratio 1.299448e+00
## condition.fctrFor parts or not working 1.289770e+00
## D.terms.n.post.stop 1.279013e+00
## prdline.my.fctriPadmini 1.185258e+00
## condition.fctrSeller refurbished 1.141570e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.119166e+00
## cellular.fctr1:carrier.fctrUnknown 1.115997e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 1.085548e+00
## prdline.my.fctriPad 3+ 1.074790e+00
## D.npnct11.log 1.028460e+00
## prdline.my.fctriPadmini:.clusterid.fctr3 9.742803e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 9.263539e-01
## D.ndgts.log 9.199387e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 9.085270e-01
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 8.016364e-01
## D.npnct12.log 7.340608e-01
## D.npnct08.log 7.259117e-01
## prdline.my.fctriPadmini 2+ 7.232466e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 6.341148e-01
## cellular.fctr1:carrier.fctrSprint 6.253784e-01
## D.npnct01.log 5.556204e-01
## carrier.fctrSprint 5.354769e-01
## prdline.my.fctriPad 1 5.323525e-01
## prdline.my.fctriPad 2 5.238119e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 4.086118e-01
## carrier.fctrT-Mobile 4.073964e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.042061e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.962993e-01
## D.npnct24.log 3.935604e-01
## cellular.fctr1:carrier.fctrT-Mobile 3.814115e-01
## D.npnct15.log 3.807822e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 3.531661e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.283063e-01
## prdline.my.fctriPadAir:D.npnct03.log 3.097530e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.966641e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.849642e-01
## D.npnct05.log 2.411519e-01
## prdline.my.fctriPadmini:D.npnct15.log 2.393818e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 2.354775e-01
## D.npnct14.log 2.344787e-01
## D.npnct16.log 2.156363e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 2.005415e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694852e-01
## D.npnct03.log 1.471806e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.381536e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 1.184789e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.551081e-02
## prdline.my.fctriPadmini:D.npnct03.log 8.979465e-02
## D.npnct06.log 6.541619e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 6.254349e-02
## prdline.my.fctriPadmini:.clusterid.fctr4 5.965600e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 4.521448e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.097444e-02
## prdline.my.fctriPad 1:D.npnct15.log 3.470296e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.968935e-02
## prdline.my.fctriPad 3+:.clusterid.fctr6 1.838658e-02
## cellular.fctr1:carrier.fctrOther 1.831673e-02
## D.npnct10.log 1.457302e-02
## D.npnct09.log 7.709772e-03
## carrier.fctrOther 6.459850e-03
## prdline.my.fctriPadmini 2+:D.npnct15.log 5.391554e-03
## prdline.my.fctriPadAir:D.npnct15.log 3.647228e-03
## prdline.my.fctriPad 3+:D.npnct03.log 3.044642e-03
## prdline.my.fctriPad 2:D.npnct03.log 2.537202e-03
## D.npnct28.log 0.000000e+00
## prdline.my.fctriPad 1:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## All.Interact.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## startprice.diff:biddable 7.295709e+01
## startprice.diff 6.287101e+01
## idseq.my 4.557866e+01
## prdline.my.fctriPadmini:idseq.my 8.119212e+00
## prdline.my.fctriPadAir:idseq.my 8.102069e+00
## D.ratio.sum.TfIdf.nwrds 6.787692e+00
## prdline.my.fctriPad 3+:idseq.my 6.463037e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.541186e+00
## prdline.my.fctriPad 1:idseq.my 5.448197e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 4.744887e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 4.653410e+00
## D.TfIdf.sum.stem.stop.Ratio 4.583426e+00
## storage.fctr64 4.562079e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 4.372017e+00
## color.fctrWhite 3.982307e+00
## D.ratio.nstopwrds.nwrds 3.747225e+00
## storage.fctrUnknown 3.656495e+00
## D.nstopwrds.log 3.501850e+00
## storage.fctr16 3.457889e+00
## D.sum.TfIdf 3.363877e+00
## color.fctrUnknown 3.345165e+00
## D.TfIdf.sum.post.stem 3.183039e+00
## cellular.fctr1 3.009368e+00
## D.TfIdf.sum.post.stop 2.954056e+00
## prdline.my.fctriPad 2:idseq.my 2.914295e+00
## condition.fctrNew 2.772069e+00
## D.nchrs.log 2.742885e+00
## D.nwrds.log 2.697258e+00
## D.nuppr.log 2.691101e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 2.637843e+00
## carrier.fctrNone 2.620120e+00
## carrier.fctrUnknown 2.136666e+00
## color.fctrSpace Gray 2.049710e+00
## condition.fctrNew other (see details) 1.824646e+00
## storage.fctr32 1.763924e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.756634e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 1.669334e+00
## D.terms.n.post.stem.log 1.590060e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 1.578635e+00
## D.terms.n.post.stem 1.567921e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.567397e+00
## D.nwrds.unq.log 1.539712e+00
## carrier.fctrVerizon 1.526833e+00
## cellular.fctr1:carrier.fctrVerizon 1.485664e+00
## D.npnct13.log 1.427327e+00
## color.fctrGold 1.423172e+00
## D.terms.n.post.stop.log 1.421139e+00
## cellular.fctrUnknown 1.374536e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.361053e+00
## prdline.my.fctriPadAir 1.359004e+00
## condition.fctrManufacturer refurbished 1.353844e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.334240e+00
## D.terms.n.stem.stop.Ratio 1.299448e+00
## condition.fctrFor parts or not working 1.289770e+00
## D.terms.n.post.stop 1.279013e+00
## prdline.my.fctriPadmini 1.185258e+00
## condition.fctrSeller refurbished 1.141570e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.119166e+00
## cellular.fctr1:carrier.fctrUnknown 1.115997e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 1.085548e+00
## prdline.my.fctriPad 3+ 1.074790e+00
## D.npnct11.log 1.028460e+00
## prdline.my.fctriPadmini:.clusterid.fctr3 9.742803e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 9.263539e-01
## D.ndgts.log 9.199387e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 9.085270e-01
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 8.016364e-01
## D.npnct12.log 7.340608e-01
## D.npnct08.log 7.259117e-01
## prdline.my.fctriPadmini 2+ 7.232466e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 6.341148e-01
## cellular.fctr1:carrier.fctrSprint 6.253784e-01
## D.npnct01.log 5.556204e-01
## carrier.fctrSprint 5.354769e-01
## prdline.my.fctriPad 1 5.323525e-01
## prdline.my.fctriPad 2 5.238119e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 4.086118e-01
## carrier.fctrT-Mobile 4.073964e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.042061e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.962993e-01
## D.npnct24.log 3.935604e-01
## cellular.fctr1:carrier.fctrT-Mobile 3.814115e-01
## D.npnct15.log 3.807822e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 3.531661e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.283063e-01
## prdline.my.fctriPadAir:D.npnct03.log 3.097530e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.966641e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.849642e-01
## D.npnct05.log 2.411519e-01
## prdline.my.fctriPadmini:D.npnct15.log 2.393818e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 2.354775e-01
## D.npnct14.log 2.344787e-01
## D.npnct16.log 2.156363e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 2.005415e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694852e-01
## D.npnct03.log 1.471806e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.381536e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 1.184789e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.551081e-02
## prdline.my.fctriPadmini:D.npnct03.log 8.979465e-02
## D.npnct06.log 6.541619e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 6.254349e-02
## prdline.my.fctriPadmini:.clusterid.fctr4 5.965600e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 4.521448e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.097444e-02
## prdline.my.fctriPad 1:D.npnct15.log 3.470296e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.968935e-02
## prdline.my.fctriPad 3+:.clusterid.fctr6 1.838658e-02
## cellular.fctr1:carrier.fctrOther 1.831673e-02
## D.npnct10.log 1.457302e-02
## D.npnct09.log 7.709772e-03
## carrier.fctrOther 6.459850e-03
## prdline.my.fctriPadmini 2+:D.npnct15.log 5.391554e-03
## prdline.my.fctriPadAir:D.npnct15.log 3.647228e-03
## prdline.my.fctriPad 3+:D.npnct03.log 3.044642e-03
## prdline.my.fctriPad 2:D.npnct03.log 2.537202e-03
## D.npnct28.log 0.000000e+00
## prdline.my.fctriPad 1:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id, : Limiting important feature scatter plots to 5 out of 49
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob
## 66 10066 N 0.076
## 594 10594 N 0.528
## 1396 11397 N 0.004
## 1802 11803 N 0.614
## 1755 11756 N 0.614
## 1509 11510 N 0.630
## 747 10747 N 0.644
## 1385 11386 N 0.654
## 127 10127 N 0.660
## 409 10409 N 0.668
## 851 10851 N 0.730
## 1835 11836 N 0.810
## 1699 11700 N 0.946
## 199 10199 N 0.956
## 1768 11769 N 0.982
## sold.fctr.predict.All.Interact.X.no.rnorm.rf
## 66 N
## 594 N
## 1396 N
## 1802 Y
## 1755 Y
## 1509 Y
## 747 Y
## 1385 Y
## 127 Y
## 409 Y
## 851 Y
## 1835 Y
## 1699 Y
## 199 Y
## 1768 Y
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.accurate
## 66 TRUE
## 594 TRUE
## 1396 TRUE
## 1802 FALSE
## 1755 FALSE
## 1509 FALSE
## 747 FALSE
## 1385 FALSE
## 127 FALSE
## 409 FALSE
## 851 FALSE
## 1835 FALSE
## 1699 FALSE
## 199 FALSE
## 1768 FALSE
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.error .label
## 66 0.000 10066
## 594 0.000 10594
## 1396 0.000 11397
## 1802 0.014 11803
## 1755 0.014 11756
## 1509 0.030 11510
## 747 0.044 10747
## 1385 0.054 11386
## 127 0.060 10127
## 409 0.068 10409
## 851 0.130 10851
## 1835 0.210 11836
## 1699 0.346 11700
## 199 0.356 10199
## 1768 0.382 11769
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob
## 1447 11448 Y 0.000
## 1358 11359 Y 0.002
## 1582 11583 Y 0.014
## 1420 11421 Y 0.022
## 1186 11186 Y 0.030
## 1381 11382 Y 0.034
## sold.fctr.predict.All.Interact.X.no.rnorm.rf
## 1447 N
## 1358 N
## 1582 N
## 1420 N
## 1186 N
## 1381 N
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.accurate
## 1447 FALSE
## 1358 FALSE
## 1582 FALSE
## 1420 FALSE
## 1186 FALSE
## 1381 FALSE
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.error
## 1447 -0.600
## 1358 -0.598
## 1582 -0.586
## 1420 -0.578
## 1186 -0.570
## 1381 -0.566
## UniqueID sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob
## 394 10394 Y 0.120
## 105 10105 Y 0.230
## 1855 11857 Y 0.242
## 1529 11530 Y 0.510
## 528 10528 N 0.668
## 1621 11622 N 0.892
## sold.fctr.predict.All.Interact.X.no.rnorm.rf
## 394 N
## 105 N
## 1855 N
## 1529 N
## 528 Y
## 1621 Y
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.accurate
## 394 FALSE
## 105 FALSE
## 1855 FALSE
## 1529 FALSE
## 528 FALSE
## 1621 FALSE
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.error
## 394 -0.480
## 105 -0.370
## 1855 -0.358
## 1529 -0.090
## 528 0.068
## 1621 0.292
## UniqueID sold.fctr sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob
## 491 10491 N 0.976
## 1768 11769 N 0.982
## 283 10283 N 0.982
## 413 10413 N 0.984
## 241 10241 N 0.986
## 488 10488 N 1.000
## sold.fctr.predict.All.Interact.X.no.rnorm.rf
## 491 Y
## 1768 Y
## 283 Y
## 413 Y
## 241 Y
## 488 Y
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.accurate
## 491 FALSE
## 1768 FALSE
## 283 FALSE
## 413 FALSE
## 241 FALSE
## 488 FALSE
## sold.fctr.predict.All.Interact.X.no.rnorm.rf.error
## 491 0.376
## 1768 0.382
## 283 0.382
## 413 0.384
## 241 0.386
## 488 0.400
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
# FN_OOB_ids <- c(4721, 4020, 693, 92)
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_feats_df$id[1:5]])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
# write.csv(glb_chunks_df, paste0(glb_out_pfx, tail(glb_chunks_df, 1)$label, "_",
# tail(glb_chunks_df, 1)$step_minor, "_chunks1.csv"),
# row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 327.364 356.67 29.306
## 13 fit.models 7 3 356.671 NA NA if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.models_3: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob"
## [2] "sold.fctr.predict.All.Interact.X.no.rnorm.rf"
## [3] "sold.fctr.predict.All.Interact.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 356.671 361.584 4.913
## 14 fit.data.training 8 0 361.584 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
stop("fit.data.training_0: Why is this happening ?")## Warning in is.na(glb_allobs_df$D.P.http): is.na() applied to non-(list or
## vector) of type 'NULL'# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## [1] "fitting model: Final.rf"
## [1] " indep_vars: D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1859 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 3718 matrix numeric
## oob.times 1859 -none- numeric
## classes 2 -none- character
## importance 140 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1859 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 140 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] " calling mypredict_mdl for fit:"
## threshold f.score
## 1 0.0 0.6325855
## 2 0.1 0.8329298
## 3 0.2 0.9419496
## 4 0.3 0.9822958
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 0.9988359
## 8 0.7 0.9505796
## 9 0.8 0.8601723
## 10 0.9 0.7766714
## 11 1.0 0.2413088## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## sold.fctr sold.fctr.predict.Final.rf.N sold.fctr.predict.Final.rf.Y
## 1 N 999 NA
## 2 Y NA 860
## Prediction
## Reference N Y
## N 999 0
## Y 0 860
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.0000000 1.0000000 0.9980176 1.0000000 0.5373857
## AccuracyPValue McnemarPValue
## 0.0000000 NaN## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rf rf
## feats
## 1 D.ratio.nstopwrds.nwrds, D.terms.n.stem.stop.Ratio, D.npnct01.log, storage.fctr, D.npnct11.log, D.npnct10.log, D.TfIdf.sum.post.stop, D.npnct13.log, D.TfIdf.sum.post.stem, D.sum.TfIdf, color.fctr, D.npnct08.log, prdline.my.fctr, D.nstopwrds.log, D.npnct16.log, D.npnct24.log, D.npnct06.log, D.npnct28.log, D.nuppr.log, D.nchrs.log, D.nwrds.log, D.npnct12.log, D.npnct09.log, D.ndgts.log, D.nwrds.unq.log, D.terms.n.post.stem.log, D.terms.n.post.stop.log, D.npnct14.log, D.terms.n.post.stem, D.terms.n.post.stop, D.npnct05.log, condition.fctr, prdline.my.fctr*idseq.my, prdline.my.fctr*D.ratio.sum.TfIdf.nwrds, prdline.my.fctr*D.TfIdf.sum.stem.stop.Ratio, prdline.my.fctr*D.npnct15.log, prdline.my.fctr*D.npnct03.log, startprice.diff*biddable, cellular.fctr*carrier.fctr, prdline.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 28.696 16.942
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8068798
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9980176 1 0.6082762
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009354327 0.0185016rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 361.584 392.647 31.063
## 15 fit.data.training 8 1 392.648 NA NA#```
#```{r fit.data.training_1, cache=FALSE}
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.6sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.Interact.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## startprice.diff 6.287101e+01
## startprice.diff:biddable 7.295709e+01
## idseq.my 4.557866e+01
## prdline.my.fctriPadAir:idseq.my 8.102069e+00
## prdline.my.fctriPadmini:idseq.my 8.119212e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.541186e+00
## prdline.my.fctriPad 3+:idseq.my 6.463037e+00
## D.ratio.sum.TfIdf.nwrds 6.787692e+00
## prdline.my.fctriPad 2:idseq.my 2.914295e+00
## D.TfIdf.sum.stem.stop.Ratio 4.583426e+00
## prdline.my.fctriPad 1:idseq.my 5.448197e+00
## D.ratio.nstopwrds.nwrds 3.747225e+00
## color.fctrWhite 3.982307e+00
## D.nstopwrds.log 3.501850e+00
## color.fctrUnknown 3.345165e+00
## storage.fctr64 4.562079e+00
## storage.fctr16 3.457889e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 4.372017e+00
## D.nchrs.log 2.742885e+00
## D.TfIdf.sum.post.stop 2.954056e+00
## D.TfIdf.sum.post.stem 3.183039e+00
## D.sum.TfIdf 3.363877e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 4.744887e+00
## condition.fctrNew 2.772069e+00
## D.nuppr.log 2.691101e+00
## storage.fctrUnknown 3.656495e+00
## D.nwrds.log 2.697258e+00
## cellular.fctr1 3.009368e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 1.669334e+00
## carrier.fctrNone 2.620120e+00
## color.fctrSpace Gray 2.049710e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 1.578635e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 1.085548e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.567397e+00
## D.npnct11.log 1.028460e+00
## carrier.fctrUnknown 2.136666e+00
## storage.fctr32 1.763924e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 2.637843e+00
## D.npnct13.log 1.427327e+00
## D.nwrds.unq.log 1.539712e+00
## prdline.my.fctriPadAir 1.359004e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.334240e+00
## D.terms.n.post.stem.log 1.590060e+00
## D.terms.n.post.stem 1.567921e+00
## D.terms.n.post.stop 1.279013e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.361053e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 4.653410e+00
## D.terms.n.post.stop.log 1.421139e+00
## condition.fctrSeller refurbished 1.141570e+00
## D.npnct15.log 3.807822e-01
## cellular.fctrUnknown 1.374536e+00
## prdline.my.fctriPad 2:.clusterid.fctr4 9.085270e-01
## condition.fctrManufacturer refurbished 1.353844e+00
## carrier.fctrVerizon 1.526833e+00
## condition.fctrFor parts or not working 1.289770e+00
## condition.fctrNew other (see details) 1.824646e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.119166e+00
## D.ndgts.log 9.199387e-01
## cellular.fctr1:carrier.fctrVerizon 1.485664e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.756634e+00
## prdline.my.fctriPad 3+ 1.074790e+00
## cellular.fctr1:carrier.fctrUnknown 1.115997e+00
## prdline.my.fctriPadmini 2+ 7.232466e-01
## color.fctrGold 1.423172e+00
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 8.016364e-01
## prdline.my.fctriPadmini 1.185258e+00
## prdline.my.fctrUnknown:.clusterid.fctr3 6.341148e-01
## D.terms.n.stem.stop.Ratio 1.299448e+00
## prdline.my.fctriPad 1 5.323525e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 9.263539e-01
## D.npnct08.log 7.259117e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 9.742803e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 5.965600e-02
## cellular.fctr1:carrier.fctrT-Mobile 3.814115e-01
## prdline.my.fctriPad 3+:.clusterid.fctr6 1.838658e-02
## prdline.my.fctriPad 2 5.238119e-01
## carrier.fctrSprint 5.354769e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 3.531661e-01
## cellular.fctr1:carrier.fctrSprint 6.253784e-01
## carrier.fctrT-Mobile 4.073964e-01
## D.npnct14.log 2.344787e-01
## D.npnct16.log 2.156363e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.283063e-01
## D.npnct01.log 5.556204e-01
## prdline.my.fctriPad 1:D.npnct15.log 3.470296e-02
## prdline.my.fctriPadAir:.clusterid.fctr2 9.551081e-02
## prdline.my.fctriPad 3+:.clusterid.fctr4 4.086118e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.962993e-01
## D.npnct24.log 3.935604e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.849642e-01
## D.npnct12.log 7.340608e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 1.184789e-01
## D.npnct06.log 6.541619e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 6.254349e-02
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694852e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 2.354775e-01
## D.npnct05.log 2.411519e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.966641e-01
## prdline.my.fctriPadmini:D.npnct15.log 2.393818e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.381536e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 2.005415e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.042061e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 4.521448e-02
## prdline.my.fctriPad 2:D.npnct03.log 2.537202e-03
## prdline.my.fctriPadAir:D.npnct03.log 3.097530e-01
## D.npnct10.log 1.457302e-02
## D.npnct03.log 1.471806e-01
## prdline.my.fctriPad 3+:D.npnct15.log 4.097444e-02
## prdline.my.fctriPadmini:D.npnct03.log 8.979465e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.968935e-02
## cellular.fctr1:carrier.fctrOther 1.831673e-02
## D.npnct09.log 7.709772e-03
## prdline.my.fctriPad 1:D.npnct03.log 0.000000e+00
## carrier.fctrOther 6.459850e-03
## D.npnct28.log 0.000000e+00
## prdline.my.fctriPad 3+:D.npnct03.log 3.044642e-03
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:D.npnct15.log 3.647228e-03
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct15.log 5.391554e-03
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## importance
## biddable 1.000000e+02
## startprice.diff 6.960528e+01
## startprice.diff:biddable 6.693838e+01
## idseq.my 4.204923e+01
## prdline.my.fctriPadAir:idseq.my 7.671632e+00
## prdline.my.fctriPadmini:idseq.my 6.375032e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.683911e+00
## prdline.my.fctriPad 3+:idseq.my 5.627514e+00
## D.ratio.sum.TfIdf.nwrds 5.029416e+00
## prdline.my.fctriPad 2:idseq.my 4.774435e+00
## D.TfIdf.sum.stem.stop.Ratio 4.764236e+00
## prdline.my.fctriPad 1:idseq.my 4.228074e+00
## D.ratio.nstopwrds.nwrds 4.055811e+00
## color.fctrWhite 3.424959e+00
## D.nstopwrds.log 3.420231e+00
## color.fctrUnknown 3.323058e+00
## storage.fctr64 3.115700e+00
## storage.fctr16 3.087964e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 3.049947e+00
## D.nchrs.log 3.032897e+00
## D.TfIdf.sum.post.stop 3.015868e+00
## D.TfIdf.sum.post.stem 3.012195e+00
## D.sum.TfIdf 2.969096e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 2.893811e+00
## condition.fctrNew 2.850164e+00
## D.nuppr.log 2.703828e+00
## storage.fctrUnknown 2.611807e+00
## D.nwrds.log 2.576687e+00
## cellular.fctr1 2.449276e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 2.363800e+00
## carrier.fctrNone 2.339635e+00
## color.fctrSpace Gray 2.323702e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 2.153269e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 2.007878e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.983204e+00
## D.npnct11.log 1.773518e+00
## carrier.fctrUnknown 1.749041e+00
## storage.fctr32 1.709455e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 1.661616e+00
## D.npnct13.log 1.641072e+00
## D.nwrds.unq.log 1.623470e+00
## prdline.my.fctriPadAir 1.601391e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.566376e+00
## D.terms.n.post.stem.log 1.498671e+00
## D.terms.n.post.stem 1.495246e+00
## D.terms.n.post.stop 1.351248e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.329742e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 1.328155e+00
## D.terms.n.post.stop.log 1.327629e+00
## condition.fctrSeller refurbished 1.323465e+00
## D.npnct15.log 1.299244e+00
## cellular.fctrUnknown 1.272499e+00
## prdline.my.fctriPad 2:.clusterid.fctr4 1.254470e+00
## condition.fctrManufacturer refurbished 1.240694e+00
## carrier.fctrVerizon 1.234290e+00
## condition.fctrFor parts or not working 1.211497e+00
## condition.fctrNew other (see details) 1.209819e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.185455e+00
## D.ndgts.log 1.184364e+00
## cellular.fctr1:carrier.fctrVerizon 1.125683e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.107954e+00
## prdline.my.fctriPad 3+ 1.091447e+00
## cellular.fctr1:carrier.fctrUnknown 1.064053e+00
## prdline.my.fctriPadmini 2+ 9.675146e-01
## color.fctrGold 9.190781e-01
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 9.080687e-01
## prdline.my.fctriPadmini 9.052589e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 8.941081e-01
## D.terms.n.stem.stop.Ratio 8.127660e-01
## prdline.my.fctriPad 1 8.040097e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 7.693623e-01
## D.npnct08.log 7.021449e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 6.830103e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 6.337059e-01
## cellular.fctr1:carrier.fctrT-Mobile 6.321343e-01
## prdline.my.fctriPad 3+:.clusterid.fctr6 6.319869e-01
## prdline.my.fctriPad 2 6.203999e-01
## carrier.fctrSprint 5.885889e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 5.527526e-01
## cellular.fctr1:carrier.fctrSprint 5.144275e-01
## carrier.fctrT-Mobile 5.016182e-01
## D.npnct14.log 4.907743e-01
## D.npnct16.log 4.716153e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.477216e-01
## D.npnct01.log 3.384943e-01
## prdline.my.fctriPad 1:D.npnct15.log 3.105568e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.071686e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 2.845234e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 2.636628e-01
## D.npnct24.log 2.612274e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.600623e-01
## D.npnct12.log 2.228584e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 2.147635e-01
## D.npnct06.log 2.138099e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 2.114139e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 2.068904e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.995621e-01
## D.npnct05.log 1.858545e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.781904e-01
## prdline.my.fctriPadmini:D.npnct15.log 1.370333e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.317783e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 1.163280e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 9.575558e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 9.357905e-02
## prdline.my.fctriPad 2:D.npnct03.log 9.355105e-02
## prdline.my.fctriPadAir:D.npnct03.log 9.102826e-02
## D.npnct10.log 8.532980e-02
## D.npnct03.log 5.802949e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.802268e-02
## prdline.my.fctriPadmini:D.npnct03.log 3.516015e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.197634e-02
## cellular.fctr1:carrier.fctrOther 8.801221e-03
## D.npnct09.log 6.856611e-03
## prdline.my.fctriPad 1:D.npnct03.log 3.536624e-03
## carrier.fctrOther 2.314881e-03
## D.npnct28.log 1.736161e-03
## prdline.my.fctriPad 3+:D.npnct03.log 9.645339e-04
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## Final.rf.importance
## biddable 1.000000e+02
## startprice.diff 6.960528e+01
## startprice.diff:biddable 6.693838e+01
## idseq.my 4.204923e+01
## prdline.my.fctriPadAir:idseq.my 7.671632e+00
## prdline.my.fctriPadmini:idseq.my 6.375032e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.683911e+00
## prdline.my.fctriPad 3+:idseq.my 5.627514e+00
## D.ratio.sum.TfIdf.nwrds 5.029416e+00
## prdline.my.fctriPad 2:idseq.my 4.774435e+00
## D.TfIdf.sum.stem.stop.Ratio 4.764236e+00
## prdline.my.fctriPad 1:idseq.my 4.228074e+00
## D.ratio.nstopwrds.nwrds 4.055811e+00
## color.fctrWhite 3.424959e+00
## D.nstopwrds.log 3.420231e+00
## color.fctrUnknown 3.323058e+00
## storage.fctr64 3.115700e+00
## storage.fctr16 3.087964e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 3.049947e+00
## D.nchrs.log 3.032897e+00
## D.TfIdf.sum.post.stop 3.015868e+00
## D.TfIdf.sum.post.stem 3.012195e+00
## D.sum.TfIdf 2.969096e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 2.893811e+00
## condition.fctrNew 2.850164e+00
## D.nuppr.log 2.703828e+00
## storage.fctrUnknown 2.611807e+00
## D.nwrds.log 2.576687e+00
## cellular.fctr1 2.449276e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 2.363800e+00
## carrier.fctrNone 2.339635e+00
## color.fctrSpace Gray 2.323702e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 2.153269e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 2.007878e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.983204e+00
## D.npnct11.log 1.773518e+00
## carrier.fctrUnknown 1.749041e+00
## storage.fctr32 1.709455e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 1.661616e+00
## D.npnct13.log 1.641072e+00
## D.nwrds.unq.log 1.623470e+00
## prdline.my.fctriPadAir 1.601391e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.566376e+00
## D.terms.n.post.stem.log 1.498671e+00
## D.terms.n.post.stem 1.495246e+00
## D.terms.n.post.stop 1.351248e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.329742e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 1.328155e+00
## D.terms.n.post.stop.log 1.327629e+00
## condition.fctrSeller refurbished 1.323465e+00
## D.npnct15.log 1.299244e+00
## cellular.fctrUnknown 1.272499e+00
## prdline.my.fctriPad 2:.clusterid.fctr4 1.254470e+00
## condition.fctrManufacturer refurbished 1.240694e+00
## carrier.fctrVerizon 1.234290e+00
## condition.fctrFor parts or not working 1.211497e+00
## condition.fctrNew other (see details) 1.209819e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.185455e+00
## D.ndgts.log 1.184364e+00
## cellular.fctr1:carrier.fctrVerizon 1.125683e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.107954e+00
## prdline.my.fctriPad 3+ 1.091447e+00
## cellular.fctr1:carrier.fctrUnknown 1.064053e+00
## prdline.my.fctriPadmini 2+ 9.675146e-01
## color.fctrGold 9.190781e-01
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 9.080687e-01
## prdline.my.fctriPadmini 9.052589e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 8.941081e-01
## D.terms.n.stem.stop.Ratio 8.127660e-01
## prdline.my.fctriPad 1 8.040097e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 7.693623e-01
## D.npnct08.log 7.021449e-01
## prdline.my.fctriPadmini:.clusterid.fctr3 6.830103e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 6.337059e-01
## cellular.fctr1:carrier.fctrT-Mobile 6.321343e-01
## prdline.my.fctriPad 3+:.clusterid.fctr6 6.319869e-01
## prdline.my.fctriPad 2 6.203999e-01
## carrier.fctrSprint 5.885889e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 5.527526e-01
## cellular.fctr1:carrier.fctrSprint 5.144275e-01
## carrier.fctrT-Mobile 5.016182e-01
## D.npnct14.log 4.907743e-01
## D.npnct16.log 4.716153e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.477216e-01
## D.npnct01.log 3.384943e-01
## prdline.my.fctriPad 1:D.npnct15.log 3.105568e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.071686e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 2.845234e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 2.636628e-01
## D.npnct24.log 2.612274e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.600623e-01
## D.npnct12.log 2.228584e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 2.147635e-01
## D.npnct06.log 2.138099e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 2.114139e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 2.068904e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.995621e-01
## D.npnct05.log 1.858545e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.781904e-01
## prdline.my.fctriPadmini:D.npnct15.log 1.370333e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.317783e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 1.163280e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 9.575558e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 9.357905e-02
## prdline.my.fctriPad 2:D.npnct03.log 9.355105e-02
## prdline.my.fctriPadAir:D.npnct03.log 9.102826e-02
## D.npnct10.log 8.532980e-02
## D.npnct03.log 5.802949e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.802268e-02
## prdline.my.fctriPadmini:D.npnct03.log 3.516015e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.197634e-02
## cellular.fctr1:carrier.fctrOther 8.801221e-03
## D.npnct09.log 6.856611e-03
## prdline.my.fctriPad 1:D.npnct03.log 3.536624e-03
## carrier.fctrOther 2.314881e-03
## D.npnct28.log 1.736161e-03
## prdline.my.fctriPad 3+:D.npnct03.log 9.645339e-04
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 49
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1358 11359 Y 0.594
## 991 10991 Y 0.596
## 1 10001 N 0.246
## 2 10002 Y 0.998
## 594 10594 N 0.152
## 1396 11397 N 0.004
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1358 N FALSE
## 991 N FALSE
## 1 N TRUE
## 2 Y TRUE
## 594 N TRUE
## 1396 N TRUE
## sold.fctr.predict.Final.rf.error .label
## 1358 -0.006 11359
## 991 -0.004 10991
## 1 0.000 10001
## 2 0.000 10002
## 594 0.000 10594
## 1396 0.000 11397
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1358 11359 Y 0.594
## 991 10991 Y 0.596
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1358 N FALSE
## 991 N FALSE
## sold.fctr.predict.Final.rf.error
## 1358 -0.006
## 991 -0.004
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.Final.rf.prob" "sold.fctr.predict.Final.rf"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 392.648 398.125 5.477
## 16 predict.data.new 9 0 398.126 NA NAStep 9.0: predict data new
# Compute final model predictions
# sav_newobs_df <- glb_newobs_df
# startprice.pred stuff
# tmp_allobs_df <- glb_get_predictions(glb_allobs_df, mdl_id=glb_fin_mdl_id,
# rsp_var_out=glb_rsp_var_out,
# prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
# glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
# "opt.prob.threshold.OOB"], NULL))
# rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
# tmp_allobs_df <- tmp_allobs_df[, c(glb_id_var, glb_rsp_var, rsp_var_out)]
# names(tmp_allobs_df)[3] <- glb_rsp_var_out
# write.csv(tmp_allobs_df, paste0(glb_out_pfx, "predict.csv"), row.names=FALSE)
##
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))## Warning in glb_get_predictions(glb_newobs_df, mdl_id = glb_fin_mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.6if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 49## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning: Removed 798 rows containing missing values (geom_point).## Warning: Removed 798 rows containing missing values (geom_point).
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## 1860 11862 <NA> 0.310
## 1863 11865 <NA> 0.532
## 2094 12096 <NA> 0.468
## 2623 12625 <NA> 0.562
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## 1860 N NA
## 1863 N NA
## 2094 N NA
## 2623 N NA
## sold.fctr.predict.Final.rf.error .label
## 1860 0 11862
## 1863 0 11865
## 2094 0 12096
## 2623 0 12625
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA NA <NA> NA
## NA.1 NA <NA> NA
## NA.2 NA <NA> NA
## NA.3 NA <NA> NA
## NA.4 NA <NA> NA
## NA.5 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA <NA> NA
## NA.1 <NA> NA
## NA.2 <NA> NA
## NA.3 <NA> NA
## NA.4 <NA> NA
## NA.5 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA NA
## NA.1 NA
## NA.2 NA
## NA.3 NA
## NA.4 NA
## NA.5 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.35 NA <NA> NA
## NA.137 NA <NA> NA
## NA.141 NA <NA> NA
## NA.431 NA <NA> NA
## NA.644 NA <NA> NA
## NA.666 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.35 <NA> NA
## NA.137 <NA> NA
## NA.141 <NA> NA
## NA.431 <NA> NA
## NA.644 <NA> NA
## NA.666 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.35 NA
## NA.137 NA
## NA.141 NA
## NA.431 NA
## NA.644 NA
## NA.666 NA
## UniqueID sold.fctr sold.fctr.predict.Final.rf.prob
## NA.792 NA <NA> NA
## NA.793 NA <NA> NA
## NA.794 NA <NA> NA
## NA.795 NA <NA> NA
## NA.796 NA <NA> NA
## NA.797 NA <NA> NA
## sold.fctr.predict.Final.rf sold.fctr.predict.Final.rf.accurate
## NA.792 <NA> NA
## NA.793 <NA> NA
## NA.794 <NA> NA
## NA.795 <NA> NA
## NA.796 <NA> NA
## NA.797 <NA> NA
## sold.fctr.predict.Final.rf.error
## NA.792 NA
## NA.793 NA
## NA.794 NA
## NA.795 NA
## NA.796 NA
## NA.797 NA## Warning: Removed 798 rows containing missing values (geom_point).
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11943,
12050, 12115, 12253, 12285, 12367, 12388, 12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11990, 11991, 11994, 11999,
12000, 12002, 12021, 12065, 12072,
12111, 12114, 12126, 12152, 12172,
12213, 12214, 12233, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
} else submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 >= 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 <= 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
if (nrow(tmp_newobs_df) > 0)
stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y"), "startprice"]) >
max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
(glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
stop("startprice for some +ve predictions > 675")
}## [1] "Prediction errors in duplicates:"
## [1] UniqueID .grpid
## [3] sold.fctr.predict.Final.rf sold.0
## [5] sold.1 sold.NA
## [7] .freq Probability1
## [9] .err
## <0 rows> (or 0-length row.names)submit_fname <- paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv")
write.csv(submit_df, submit_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words TfIDf terms for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
TfIdf_mtrx <- glb_post_stem_words_TfIdf_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(TfIdf_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(TfIdf_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#TfIdf_mtrx[2442, TfIdf_mtrx[2442, ] > 0]
print(sprintf(" Top_n post_stem_words TfIDf terms for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
top_n_vctr <- tmp_df$term[1:glb_top_n[[txt_var]]]
tmp_freq1_df <- subset(tmp_df, freq == 1)
tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words TfIDf terms for descr.my:"
## TfIdf term freq pos
## condit 209.2617 condit 496 111
## use 147.7914 use 291 501
## scratch 129.1467 scratch 286 409
## new 126.1758 new 156 316
## good 121.5866 good 197 213
## ipad 108.6364 ipad 232 249
## TfIdf term freq pos
## origin 42.799283 origin 56 332
## came 4.107736 came 4 85
## necessari 2.305685 necessari 2 312
## swipe 2.275117 swipe 1 465
## titl 2.075117 titl 2 481
## buyer 1.625083 buyer 1 83
## TfIdf term freq pos
## marksabsolut 1.421948 marksabsolut 1 292
## often 1.421948 often 1 326
## 360 1.263954 360 1 9
## 975 1.137558 975 1 15
## mic 1.137558 mic 1 298
## 79in 1.034144 79in 1 14
## UniqueID
## 520 10520
## descr.my
## 520 Apple iPad mini 1st Generation 16GB, Wi- Fi, 7.9in - spacegray, great condition comes with the
## [1] 65
## [1] " Top_n post_stem_words TfIDf terms for descr.my:"
## [1] TfIdf term freq pos top_n
## <0 rows> (or 0-length row.names)if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.6print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.Interact.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 974 76print(dsp_models_df)## model_id max.Accuracy.OOB max.auc.OOB
## 18 All.Interact.X.no.rnorm.rf 0.8146893 0.8559487
## 13 All.X.no.rnorm.rf 0.8135593 0.8637792
## 11 All.X.glmnet 0.8079096 0.8631938
## 6 Max.cor.Y.glm 0.8033898 0.8633582
## 7 Interact.High.cor.Y.glm 0.8011299 0.8615815
## 8 Low.cor.X.glm 0.8000000 0.8465571
## 10 All.X.bayesglm 0.7977401 0.8462850
## 9 All.X.glm 0.7966102 0.8431528
## 16 All.Interact.X.glmnet 0.7943503 0.8510347
## 15 All.Interact.X.bayesglm 0.7943503 0.8364519
## 14 All.Interact.X.glm 0.7864407 0.8344904
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.7853107 0.8187625
## 21 csm.glmnet 0.7785311 0.7816380
## 5 Max.cor.Y.rpart 0.7774011 0.8129705
## 12 All.X.no.rnorm.rpart 0.7774011 0.8129705
## 17 All.Interact.X.no.rnorm.rpart 0.7774011 0.8129705
## 22 csm.rpart 0.7774011 0.7742747
## 20 csm.bayesglm 0.7514124 0.7749705
## 19 csm.glm 0.7322034 0.7577818
## 23 csm.rf 0.7197740 0.7665417
## 1 MFO.myMFO_classfr 0.5367232 0.5000000
## 3 Max.cor.Y.cv.0.rpart 0.5367232 0.5000000
## 2 Random.myrandom_classfr 0.4632768 0.5191913
## max.Kappa.OOB min.aic.fit opt.prob.threshold.OOB
## 18 0.6215582 NA 0.6
## 13 0.6218781 NA 0.5
## 11 0.6095656 NA 0.6
## 6 0.6006483 919.4841 0.6
## 7 0.5956491 931.8178 0.6
## 8 0.5951959 938.3505 0.5
## 10 0.5906219 992.7758 0.5
## 9 0.5888183 948.3929 0.5
## 16 0.5828499 NA 0.5
## 15 0.5815820 1023.1966 0.6
## 14 0.5676063 942.9274 0.5
## 4 0.5650993 NA 0.5
## 21 0.5533181 NA 0.4
## 5 0.5506630 NA 0.3
## 12 0.5506630 NA 0.3
## 17 0.5506630 NA 0.3
## 22 0.5506630 NA 0.7
## 20 0.4999615 1106.2071 0.4
## 19 0.4624886 1061.9948 0.4
## 23 0.4406158 NA 0.3
## 1 0.0000000 NA 0.5
## 3 0.0000000 NA 0.5
## 2 0.0000000 NA 0.4if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, glb_rsp_var,
predct_error_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "error.abs.OOB"
sOOB_ctgry_df <- dplyr::group_by(tmp_OOBobs_df, prdline.my)
sOOB_ctgry_df <- dplyr::count(sOOB_ctgry_df,
startprice.OOB.sum = sum(startprice),
err.abs.OOB.sum = sum(error.abs.OOB),
err.abs.OOB.mean = mean(error.abs.OOB))
names(sOOB_ctgry_df)[4] <- ".n.OOB"
sOOB_ctgry_df <- dplyr::ungroup(sOOB_ctgry_df)
#intersect(names(glb_ctgry_df), names(sOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, sOOB_ctgry_df, all=TRUE)
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
model_id_method=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print(glb_OOBobs_df[(glb_OOBobs_df$prdline.my == "iPadAir") &
!(glb_OOBobs_df[, predct_accurate_var_name]),
c(glb_id_var, glb_rsp_var_raw,
#"description"
"biddable", "startprice", "condition"
)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "All.Interact.X.no.rnorm.rf OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 439 36
## Y 128 282
## prdline.my .n.OOB .n.Tst .freqRatio.Tst .freqRatio.OOB
## 3 iPad 2 171 154 0.1929825 0.1932203
## 5 iPadAir 151 137 0.1716792 0.1706215
## 1 Unknown 97 87 0.1090226 0.1096045
## 4 iPad 3+ 136 123 0.1541353 0.1536723
## 2 iPad 1 99 89 0.1115288 0.1118644
## 6 iPadmini 127 114 0.1428571 0.1435028
## 7 iPadmini 2+ 104 94 0.1177945 0.1175141
## accurate.OOB.FALSE accurate.OOB.TRUE max.accuracy.OOB
## 3 29 142 0.8304094
## 5 29 122 0.8079470
## 1 23 74 0.7628866
## 4 22 114 0.8382353
## 2 21 78 0.7878788
## 6 21 106 0.8346457
## 7 19 85 0.8173077
## UniqueID sold biddable startprice condition
## 246 10246 0 1 400.00 Used
## 261 10261 0 1 250.00 Used
## 738 10738 0 1 350.00 New
## 1320 11321 0 1 600.00 New
## 19 10019 1 0 375.00 Used
## 109 10109 1 0 339.99 New
## 205 10205 1 0 415.00 Used
## 277 10277 1 0 300.00 Used
## 342 10342 1 1 295.00 Used
## 467 10467 1 1 249.98 Used
## 572 10572 1 1 185.00 Used
## 577 10577 1 1 279.00 Used
## 625 10625 1 0 559.99 Used
## 675 10675 1 0 280.00 Used
## 976 10976 1 1 350.00 Used
## 1129 11129 1 1 350.00 New other (see details)
## 1200 11200 1 0 379.99 Used
## 1212 11212 1 0 450.00 New
## 1225 11225 1 0 499.99 Used
## 1259 11260 1 1 260.00 New other (see details)
## 1294 11295 1 0 425.00 New
## 1349 11350 1 0 499.00 Used
## 1353 11354 1 0 300.00 Used
## 1381 11382 1 0 439.99 New
## 1496 11497 1 0 320.00 Manufacturer refurbished
## 1521 11522 1 0 419.00 New
## 1604 11605 1 0 229.00 For parts or not working
## 1790 11791 1 1 349.99 New
## 1853 11855 1 0 424.99 Useddsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_var]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
# if (glb_is_classification) {
# print("FN_OOB_ids:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
# union(myfind_chr_cols_df(glb_OOBobs_df),
# grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
# }
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.Interact.X.no.rnorm.rf.importance
## biddable 1.000000e+02
## startprice.diff:biddable 7.295709e+01
## startprice.diff 6.287101e+01
## idseq.my 4.557866e+01
## prdline.my.fctriPadmini:idseq.my 8.119212e+00
## prdline.my.fctriPadAir:idseq.my 8.102069e+00
## D.ratio.sum.TfIdf.nwrds 6.787692e+00
## prdline.my.fctriPad 3+:idseq.my 6.463037e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.541186e+00
## prdline.my.fctriPad 1:idseq.my 5.448197e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 4.744887e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 4.653410e+00
## D.TfIdf.sum.stem.stop.Ratio 4.583426e+00
## storage.fctr64 4.562079e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 4.372017e+00
## color.fctrWhite 3.982307e+00
## D.ratio.nstopwrds.nwrds 3.747225e+00
## storage.fctrUnknown 3.656495e+00
## D.nstopwrds.log 3.501850e+00
## storage.fctr16 3.457889e+00
## D.sum.TfIdf 3.363877e+00
## color.fctrUnknown 3.345165e+00
## D.TfIdf.sum.post.stem 3.183039e+00
## cellular.fctr1 3.009368e+00
## D.TfIdf.sum.post.stop 2.954056e+00
## prdline.my.fctriPad 2:idseq.my 2.914295e+00
## condition.fctrNew 2.772069e+00
## D.nchrs.log 2.742885e+00
## D.nwrds.log 2.697258e+00
## D.nuppr.log 2.691101e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 2.637843e+00
## carrier.fctrNone 2.620120e+00
## carrier.fctrUnknown 2.136666e+00
## color.fctrSpace Gray 2.049710e+00
## condition.fctrNew other (see details) 1.824646e+00
## storage.fctr32 1.763924e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.756634e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 1.669334e+00
## D.terms.n.post.stem.log 1.590060e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 1.578635e+00
## D.terms.n.post.stem 1.567921e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.567397e+00
## D.nwrds.unq.log 1.539712e+00
## carrier.fctrVerizon 1.526833e+00
## cellular.fctr1:carrier.fctrVerizon 1.485664e+00
## D.npnct13.log 1.427327e+00
## color.fctrGold 1.423172e+00
## D.terms.n.post.stop.log 1.421139e+00
## cellular.fctrUnknown 1.374536e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.361053e+00
## prdline.my.fctriPadAir 1.359004e+00
## condition.fctrManufacturer refurbished 1.353844e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.334240e+00
## D.terms.n.stem.stop.Ratio 1.299448e+00
## condition.fctrFor parts or not working 1.289770e+00
## D.terms.n.post.stop 1.279013e+00
## prdline.my.fctriPadmini 1.185258e+00
## condition.fctrSeller refurbished 1.141570e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.119166e+00
## cellular.fctr1:carrier.fctrUnknown 1.115997e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 1.085548e+00
## prdline.my.fctriPad 3+ 1.074790e+00
## D.npnct11.log 1.028460e+00
## prdline.my.fctriPadmini:.clusterid.fctr3 9.742803e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 9.263539e-01
## D.ndgts.log 9.199387e-01
## prdline.my.fctriPad 2:.clusterid.fctr4 9.085270e-01
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 8.016364e-01
## D.npnct12.log 7.340608e-01
## D.npnct08.log 7.259117e-01
## prdline.my.fctriPadmini 2+ 7.232466e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 6.341148e-01
## cellular.fctr1:carrier.fctrSprint 6.253784e-01
## D.npnct01.log 5.556204e-01
## carrier.fctrSprint 5.354769e-01
## prdline.my.fctriPad 1 5.323525e-01
## prdline.my.fctriPad 2 5.238119e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 4.086118e-01
## carrier.fctrT-Mobile 4.073964e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 4.042061e-01
## prdline.my.fctriPad 1:.clusterid.fctr2 3.962993e-01
## D.npnct24.log 3.935604e-01
## cellular.fctr1:carrier.fctrT-Mobile 3.814115e-01
## D.npnct15.log 3.807822e-01
## prdline.my.fctriPad 2:.clusterid.fctr2 3.531661e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.283063e-01
## prdline.my.fctriPadAir:D.npnct03.log 3.097530e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 2.966641e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.849642e-01
## D.npnct05.log 2.411519e-01
## prdline.my.fctriPadmini:D.npnct15.log 2.393818e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 2.354775e-01
## D.npnct14.log 2.344787e-01
## D.npnct16.log 2.156363e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 2.005415e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 1.694852e-01
## D.npnct03.log 1.471806e-01
## prdline.my.fctriPadAir:.clusterid.fctr3 1.381536e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 1.184789e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 9.551081e-02
## prdline.my.fctriPadmini:D.npnct03.log 8.979465e-02
## D.npnct06.log 6.541619e-02
## prdline.my.fctriPadmini:.clusterid.fctr5 6.254349e-02
## prdline.my.fctriPadmini:.clusterid.fctr4 5.965600e-02
## prdline.my.fctriPad 1:.clusterid.fctr3 4.521448e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.097444e-02
## prdline.my.fctriPad 1:D.npnct15.log 3.470296e-02
## prdline.my.fctriPad 2:D.npnct15.log 1.968935e-02
## prdline.my.fctriPad 3+:.clusterid.fctr6 1.838658e-02
## cellular.fctr1:carrier.fctrOther 1.831673e-02
## D.npnct10.log 1.457302e-02
## D.npnct09.log 7.709772e-03
## carrier.fctrOther 6.459850e-03
## prdline.my.fctriPadmini 2+:D.npnct15.log 5.391554e-03
## prdline.my.fctriPadAir:D.npnct15.log 3.647228e-03
## prdline.my.fctriPad 3+:D.npnct03.log 3.044642e-03
## prdline.my.fctriPad 2:D.npnct03.log 2.537202e-03
## prdline.my.fctriPad 1:D.npnct03.log 0.000000e+00
## D.npnct28.log 0.000000e+00
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## importance
## biddable 1.000000e+02
## startprice.diff:biddable 6.693838e+01
## startprice.diff 6.960528e+01
## idseq.my 4.204923e+01
## prdline.my.fctriPadmini:idseq.my 6.375032e+00
## prdline.my.fctriPadAir:idseq.my 7.671632e+00
## D.ratio.sum.TfIdf.nwrds 5.029416e+00
## prdline.my.fctriPad 3+:idseq.my 5.627514e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.683911e+00
## prdline.my.fctriPad 1:idseq.my 4.228074e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 2.893811e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 1.328155e+00
## D.TfIdf.sum.stem.stop.Ratio 4.764236e+00
## storage.fctr64 3.115700e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 3.049947e+00
## color.fctrWhite 3.424959e+00
## D.ratio.nstopwrds.nwrds 4.055811e+00
## storage.fctrUnknown 2.611807e+00
## D.nstopwrds.log 3.420231e+00
## storage.fctr16 3.087964e+00
## D.sum.TfIdf 2.969096e+00
## color.fctrUnknown 3.323058e+00
## D.TfIdf.sum.post.stem 3.012195e+00
## cellular.fctr1 2.449276e+00
## D.TfIdf.sum.post.stop 3.015868e+00
## prdline.my.fctriPad 2:idseq.my 4.774435e+00
## condition.fctrNew 2.850164e+00
## D.nchrs.log 3.032897e+00
## D.nwrds.log 2.576687e+00
## D.nuppr.log 2.703828e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 1.661616e+00
## carrier.fctrNone 2.339635e+00
## carrier.fctrUnknown 1.749041e+00
## color.fctrSpace Gray 2.323702e+00
## condition.fctrNew other (see details) 1.209819e+00
## storage.fctr32 1.709455e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.107954e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 2.363800e+00
## D.terms.n.post.stem.log 1.498671e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 2.153269e+00
## D.terms.n.post.stem 1.495246e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.983204e+00
## D.nwrds.unq.log 1.623470e+00
## carrier.fctrVerizon 1.234290e+00
## cellular.fctr1:carrier.fctrVerizon 1.125683e+00
## D.npnct13.log 1.641072e+00
## color.fctrGold 9.190781e-01
## D.terms.n.post.stop.log 1.327629e+00
## cellular.fctrUnknown 1.272499e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.329742e+00
## prdline.my.fctriPadAir 1.601391e+00
## condition.fctrManufacturer refurbished 1.240694e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.566376e+00
## D.terms.n.stem.stop.Ratio 8.127660e-01
## condition.fctrFor parts or not working 1.211497e+00
## D.terms.n.post.stop 1.351248e+00
## prdline.my.fctriPadmini 9.052589e-01
## condition.fctrSeller refurbished 1.323465e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.185455e+00
## cellular.fctr1:carrier.fctrUnknown 1.064053e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 2.007878e+00
## prdline.my.fctriPad 3+ 1.091447e+00
## D.npnct11.log 1.773518e+00
## prdline.my.fctriPadmini:.clusterid.fctr3 6.830103e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 7.693623e-01
## D.ndgts.log 1.184364e+00
## prdline.my.fctriPad 2:.clusterid.fctr4 1.254470e+00
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 9.080687e-01
## D.npnct12.log 2.228584e-01
## D.npnct08.log 7.021449e-01
## prdline.my.fctriPadmini 2+ 9.675146e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 8.941081e-01
## cellular.fctr1:carrier.fctrSprint 5.144275e-01
## D.npnct01.log 3.384943e-01
## carrier.fctrSprint 5.885889e-01
## prdline.my.fctriPad 1 8.040097e-01
## prdline.my.fctriPad 2 6.203999e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 2.845234e-01
## carrier.fctrT-Mobile 5.016182e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 9.575558e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 2.636628e-01
## D.npnct24.log 2.612274e-01
## cellular.fctr1:carrier.fctrT-Mobile 6.321343e-01
## D.npnct15.log 1.299244e+00
## prdline.my.fctriPad 2:.clusterid.fctr2 5.527526e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.477216e-01
## prdline.my.fctriPadAir:D.npnct03.log 9.102826e-02
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.781904e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.600623e-01
## D.npnct05.log 1.858545e-01
## prdline.my.fctriPadmini:D.npnct15.log 1.370333e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.995621e-01
## D.npnct14.log 4.907743e-01
## D.npnct16.log 4.716153e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 1.163280e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 2.068904e-01
## D.npnct03.log 5.802949e-02
## prdline.my.fctriPadAir:.clusterid.fctr3 1.317783e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 2.147635e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.071686e-01
## prdline.my.fctriPadmini:D.npnct03.log 3.516015e-02
## D.npnct06.log 2.138099e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 2.114139e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 6.337059e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 9.357905e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.802268e-02
## prdline.my.fctriPad 1:D.npnct15.log 3.105568e-01
## prdline.my.fctriPad 2:D.npnct15.log 1.197634e-02
## prdline.my.fctriPad 3+:.clusterid.fctr6 6.319869e-01
## cellular.fctr1:carrier.fctrOther 8.801221e-03
## D.npnct10.log 8.532980e-02
## D.npnct09.log 6.856611e-03
## carrier.fctrOther 2.314881e-03
## prdline.my.fctriPadmini 2+:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadAir:D.npnct15.log 0.000000e+00
## prdline.my.fctriPad 3+:D.npnct03.log 9.645339e-04
## prdline.my.fctriPad 2:D.npnct03.log 9.355105e-02
## prdline.my.fctriPad 1:D.npnct03.log 3.536624e-03
## D.npnct28.log 1.736161e-03
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00
## Final.rf.importance
## biddable 1.000000e+02
## startprice.diff:biddable 6.693838e+01
## startprice.diff 6.960528e+01
## idseq.my 4.204923e+01
## prdline.my.fctriPadmini:idseq.my 6.375032e+00
## prdline.my.fctriPadAir:idseq.my 7.671632e+00
## D.ratio.sum.TfIdf.nwrds 5.029416e+00
## prdline.my.fctriPad 3+:idseq.my 5.627514e+00
## prdline.my.fctriPadmini 2+:idseq.my 5.683911e+00
## prdline.my.fctriPad 1:idseq.my 4.228074e+00
## prdline.my.fctriPad 3+:D.TfIdf.sum.stem.stop.Ratio 2.893811e+00
## prdline.my.fctriPad 2:D.ratio.sum.TfIdf.nwrds 1.328155e+00
## D.TfIdf.sum.stem.stop.Ratio 4.764236e+00
## storage.fctr64 3.115700e+00
## prdline.my.fctriPadAir:D.TfIdf.sum.stem.stop.Ratio 3.049947e+00
## color.fctrWhite 3.424959e+00
## D.ratio.nstopwrds.nwrds 4.055811e+00
## storage.fctrUnknown 2.611807e+00
## D.nstopwrds.log 3.420231e+00
## storage.fctr16 3.087964e+00
## D.sum.TfIdf 2.969096e+00
## color.fctrUnknown 3.323058e+00
## D.TfIdf.sum.post.stem 3.012195e+00
## cellular.fctr1 2.449276e+00
## D.TfIdf.sum.post.stop 3.015868e+00
## prdline.my.fctriPad 2:idseq.my 4.774435e+00
## condition.fctrNew 2.850164e+00
## D.nchrs.log 3.032897e+00
## D.nwrds.log 2.576687e+00
## D.nuppr.log 2.703828e+00
## prdline.my.fctriPadmini:D.TfIdf.sum.stem.stop.Ratio 1.661616e+00
## carrier.fctrNone 2.339635e+00
## carrier.fctrUnknown 1.749041e+00
## color.fctrSpace Gray 2.323702e+00
## condition.fctrNew other (see details) 1.209819e+00
## storage.fctr32 1.709455e+00
## prdline.my.fctrUnknown:.clusterid.fctr2 1.107954e+00
## prdline.my.fctriPad 1:D.TfIdf.sum.stem.stop.Ratio 2.363800e+00
## D.terms.n.post.stem.log 1.498671e+00
## prdline.my.fctriPad 1:D.ratio.sum.TfIdf.nwrds 2.153269e+00
## D.terms.n.post.stem 1.495246e+00
## prdline.my.fctriPadmini 2+:D.TfIdf.sum.stem.stop.Ratio 1.983204e+00
## D.nwrds.unq.log 1.623470e+00
## carrier.fctrVerizon 1.234290e+00
## cellular.fctr1:carrier.fctrVerizon 1.125683e+00
## D.npnct13.log 1.641072e+00
## color.fctrGold 9.190781e-01
## D.terms.n.post.stop.log 1.327629e+00
## cellular.fctrUnknown 1.272499e+00
## cellular.fctrUnknown:carrier.fctrUnknown 1.329742e+00
## prdline.my.fctriPadAir 1.601391e+00
## condition.fctrManufacturer refurbished 1.240694e+00
## prdline.my.fctriPad 3+:D.ratio.sum.TfIdf.nwrds 1.566376e+00
## D.terms.n.stem.stop.Ratio 8.127660e-01
## condition.fctrFor parts or not working 1.211497e+00
## D.terms.n.post.stop 1.351248e+00
## prdline.my.fctriPadmini 9.052589e-01
## condition.fctrSeller refurbished 1.323465e+00
## prdline.my.fctriPadmini:D.ratio.sum.TfIdf.nwrds 1.185455e+00
## cellular.fctr1:carrier.fctrUnknown 1.064053e+00
## prdline.my.fctriPad 2:D.TfIdf.sum.stem.stop.Ratio 2.007878e+00
## prdline.my.fctriPad 3+ 1.091447e+00
## D.npnct11.log 1.773518e+00
## prdline.my.fctriPadmini:.clusterid.fctr3 6.830103e-01
## prdline.my.fctriPadmini 2+:D.ratio.sum.TfIdf.nwrds 7.693623e-01
## D.ndgts.log 1.184364e+00
## prdline.my.fctriPad 2:.clusterid.fctr4 1.254470e+00
## prdline.my.fctriPadAir:D.ratio.sum.TfIdf.nwrds 9.080687e-01
## D.npnct12.log 2.228584e-01
## D.npnct08.log 7.021449e-01
## prdline.my.fctriPadmini 2+ 9.675146e-01
## prdline.my.fctrUnknown:.clusterid.fctr3 8.941081e-01
## cellular.fctr1:carrier.fctrSprint 5.144275e-01
## D.npnct01.log 3.384943e-01
## carrier.fctrSprint 5.885889e-01
## prdline.my.fctriPad 1 8.040097e-01
## prdline.my.fctriPad 2 6.203999e-01
## prdline.my.fctriPad 3+:.clusterid.fctr4 2.845234e-01
## carrier.fctrT-Mobile 5.016182e-01
## prdline.my.fctriPad 1:.clusterid.fctr4 9.575558e-02
## prdline.my.fctriPad 1:.clusterid.fctr2 2.636628e-01
## D.npnct24.log 2.612274e-01
## cellular.fctr1:carrier.fctrT-Mobile 6.321343e-01
## D.npnct15.log 1.299244e+00
## prdline.my.fctriPad 2:.clusterid.fctr2 5.527526e-01
## prdline.my.fctriPad 3+:.clusterid.fctr2 3.477216e-01
## prdline.my.fctriPadAir:D.npnct03.log 9.102826e-02
## prdline.my.fctriPadmini 2+:.clusterid.fctr2 1.781904e-01
## prdline.my.fctriPadmini:.clusterid.fctr2 2.600623e-01
## D.npnct05.log 1.858545e-01
## prdline.my.fctriPadmini:D.npnct15.log 1.370333e-01
## prdline.my.fctriPad 3+:.clusterid.fctr3 1.995621e-01
## D.npnct14.log 4.907743e-01
## D.npnct16.log 4.716153e-01
## prdline.my.fctriPad 2:.clusterid.fctr3 1.163280e-01
## prdline.my.fctriPadmini 2+:.clusterid.fctr3 2.068904e-01
## D.npnct03.log 5.802949e-02
## prdline.my.fctriPadAir:.clusterid.fctr3 1.317783e-01
## prdline.my.fctriPad 3+:.clusterid.fctr5 2.147635e-01
## prdline.my.fctriPadAir:.clusterid.fctr2 3.071686e-01
## prdline.my.fctriPadmini:D.npnct03.log 3.516015e-02
## D.npnct06.log 2.138099e-01
## prdline.my.fctriPadmini:.clusterid.fctr5 2.114139e-01
## prdline.my.fctriPadmini:.clusterid.fctr4 6.337059e-01
## prdline.my.fctriPad 1:.clusterid.fctr3 9.357905e-02
## prdline.my.fctriPad 3+:D.npnct15.log 4.802268e-02
## prdline.my.fctriPad 1:D.npnct15.log 3.105568e-01
## prdline.my.fctriPad 2:D.npnct15.log 1.197634e-02
## prdline.my.fctriPad 3+:.clusterid.fctr6 6.319869e-01
## cellular.fctr1:carrier.fctrOther 8.801221e-03
## D.npnct10.log 8.532980e-02
## D.npnct09.log 6.856611e-03
## carrier.fctrOther 2.314881e-03
## prdline.my.fctriPadmini 2+:D.npnct15.log 0.000000e+00
## prdline.my.fctriPadAir:D.npnct15.log 0.000000e+00
## prdline.my.fctriPad 3+:D.npnct03.log 9.645339e-04
## prdline.my.fctriPad 2:D.npnct03.log 9.355105e-02
## prdline.my.fctriPad 1:D.npnct03.log 3.536624e-03
## D.npnct28.log 1.736161e-03
## cellular.fctr1:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrNone 0.000000e+00
## cellular.fctrUnknown:carrier.fctrOther 0.000000e+00
## cellular.fctrUnknown:carrier.fctrSprint 0.000000e+00
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000e+00
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr4 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr5 0.000000e+00
## prdline.my.fctrUnknown:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 1:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPad 2:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadAir:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr4 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr5 0.000000e+00
## prdline.my.fctriPadmini 2+:.clusterid.fctr6 0.000000e+00
## prdline.my.fctriPadmini 2+:D.npnct03.log 0.000000e+00
## prdline.my.fctriPadmini:.clusterid.fctr6 0.000000e+00print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 630 168# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
if (length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
if (length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0)
print(diff)
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
if (length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)
print(diff)
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 398.126 404.414 6.288
## 17 display.session.info 10 0 404.414 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 161.264 327.363 166.099
## 5 extract.features 3 0 40.685 125.107 84.422
## 14 fit.data.training 8 0 361.584 392.647 31.063
## 12 fit.models 7 2 327.364 356.670 29.306
## 10 fit.models 7 0 134.301 161.263 26.962
## 2 inspect.data 2 0 26.493 39.269 12.776
## 1 import.data 1 0 14.411 26.493 12.082
## 16 predict.data.new 9 0 398.126 404.414 6.288
## 15 fit.data.training 8 1 392.648 398.125 5.477
## 13 fit.models 7 3 356.671 361.584 4.913
## 8 select.features 5 0 129.395 133.412 4.017
## 7 manage.missing.data 4 1 126.074 129.394 3.320
## 6 cluster.data 4 0 125.107 126.073 0.966
## 9 partition.data.training 6 0 133.412 134.301 0.889
## 3 scrub.data 2 1 39.269 40.040 0.771
## 4 transform.data 2 2 40.041 40.684 0.643
## duration
## 11 166.099
## 5 84.422
## 14 31.063
## 12 29.306
## 10 26.962
## 2 12.776
## 1 12.082
## 16 6.288
## 15 5.477
## 13 4.913
## 8 4.017
## 7 3.320
## 6 0.966
## 9 0.889
## 3 0.771
## 4 0.643
## [1] "Total Elapsed Time: 404.414 secs"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] C/en_US.UTF-8/C/C/C/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] randomForest_4.6-10 glmnet_2.0-2 arm_1.8-6
## [4] lme4_1.1-8 Matrix_1.2-2 MASS_7.3-43
## [7] rpart.plot_1.5.2 rpart_4.1-10 ROCR_1.0-7
## [10] gplots_2.17.0 sampling_2.7 tidyr_0.2.0
## [13] entropy_1.2.1 dynamicTreeCut_1.62 proxy_0.4-15
## [16] tm_0.6-2 NLP_0.1-8 stringr_1.0.0
## [19] dplyr_0.4.2 plyr_1.8.3 sqldf_0.4-10
## [22] RSQLite_1.0.0 DBI_0.3.1 gsubfn_0.6-6
## [25] proto_0.3-10 reshape2_1.4.1 gdata_2.17.0
## [28] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [31] doBy_4.5-13 survival_2.38-3 caret_6.0-52
## [34] ggplot2_1.0.1 lattice_0.20-33
##
## loaded via a namespace (and not attached):
## [1] splines_3.2.1 gtools_3.5.0 assertthat_0.1
## [4] stats4_3.2.1 yaml_2.1.13 slam_0.1-32
## [7] quantreg_5.11 pROC_1.8 chron_2.3-47
## [10] digest_0.6.8 RColorBrewer_1.1-2 minqa_1.2.4
## [13] colorspace_1.2-6 htmltools_0.2.6 lpSolve_5.6.11
## [16] BradleyTerry2_1.0-6 SparseM_1.6 scales_0.2.5
## [19] brglm_0.5-9 mgcv_1.8-7 car_2.0-25
## [22] nnet_7.3-10 lazyeval_0.1.10 pbkrtest_0.4-2
## [25] magrittr_1.5 evaluate_0.7 nlme_3.1-121
## [28] class_7.3-13 tools_3.2.1 formatR_1.2
## [31] munsell_0.4.2 compiler_3.2.1 e1071_1.6-6
## [34] caTools_1.17.1 nloptr_1.0.4 bitops_1.0-6
## [37] labeling_0.3 rmarkdown_0.7 gtable_0.1.2
## [40] codetools_0.2-14 abind_1.4-3 R6_2.1.0
## [43] knitr_1.10.5 KernSmooth_2.23-15 stringi_0.5-5
## [46] Rcpp_0.12.0 coda_0.17-1